scholarly journals PERUBAHAN TUTUPAN LAHAN HUTAN LINDUNG GUNUNG GUNTUR TAHUN 2014 SAMPAI DENGAN TAHUN 2017

2020 ◽  
Vol 21 (1) ◽  
pp. 30
Author(s):  
Prasetyo Widodo ◽  
Abdul Japar Sidik
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Image Interpretation ◽  
Landsat 8 ◽  
Land Protection ◽  
Community Activities ◽  
Protected Forests ◽  
Open Land

High pressure by community activities on the existence of forests, especially protected forests that affect the quality of the environment that can cause a disaster, such as the occurrence of flash floods that occurred in 2016 in Garut regency, cannot be separated from damage to the upstream cover of cimanuk-citanduy. This prompted investigators to analyze the three year change of land protection prevailing in Mt. Guntur RPH Simpang BKPH Bayongbong. The objective of research is to calculate how large changes land cover area in Mt. Guntur Protected Area (MGPA), RPH Simpang BKPH Bayongbong KPH Garut in three years. The data collected on July to August 2017 by geographic information system (GIS) and satellite image. The results of land cover interpretation by landsat 8 OLI image 2014 and 2017 describe the condition of land use and land cover change in MGPA. Land cover of MGPA dominated by shrub (B) is 287.58 Ha (57.52%) at 2014 and 202.89 Ha (40.58%) at 2017, so deforestation as three years is 31.24 Ha or 32.13%. The results of ground check there is a land use change to open land and farming dryland. According to data of image interpretation at 2017, the open land is 20.03 Ha but after ground checking is 20.51 Ha. The reduction of it based on data of image interpretation at 2017 is 200.33 Ha to 201.85 Ha after ground checking.

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.

scholarly journals Urban Bare Land Classification Using NDBaI Index Based on Combination of Sentinel 2 MSI and Landsat 8 Multiresolution Images

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Trinh Le Hung
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Urban Land ◽  
Landsat 8 ◽  
Land Classification ◽  
Infrared Band ◽  
Urban Land Cover ◽  
Bare Land ◽  
Sentinel 2

The classification of urban land cover/land use is a difficult task due to the complexity in the structure of the urban surface. This paper presents the method of combining of Sentinel 2 MSI and Landsat 8 multi-resolution satellite image data for urban bare land classification based on NDBaI index. Two images of Sentinel 2 and Landsat 8 acquired closely together, were used to calculate the NDBaI index, in which sortware infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) of Landsat 8 image were used to improve the spatial resolution of NDBaI index. The results obtained from two experimental areas showed that, the total accuracy of classifying bare land from the NDBaI index which calculated by the proposed method increased by about 6% compared to the method using the NDBaI index, which is calculated using only Landsat 8 data. The results obtained in this study contribute to improving the efficiency of using free remote sensing data in urban land cover/land use classification.

scholarly journals NDVI Based Assessment of Land Cover Changes Using Remote Sensing and GIS (A case study of Srinagar district, Kashmir)

2020 ◽  
Author(s):  
Perminder Singh ◽  
Ovais Javeed
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Spectral Signature ◽  
Landsat 8 ◽  
Thematic Mapping

Normalized Difference Vegetation Index (NDVI) is an index of greenness or photosynthetic activity in a plant. It is a technique of obtaining  various features based upon their spectral signature  such as vegetation index, land cover classification, urban areas and remaining areas presented in the image. The NDVI differencing method using Landsat thematic mapping images and Landsat oli  was implemented to assess the chane in vegetation cover from 2001to 2017. In the present study, Landsat TM images of 2001 and landsat 8 of 2017 were used to extract NDVI values. The NDVI values calculated from the satellite image of the year 2001 ranges from 0.62 to -0.41 and that of the year 2017 shows a significant change across the whole region and its value ranges from 0.53 to -0.10 based upon their spectral signature .This technique is also  used for the mapping of changes in land use  and land cover.  NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as -0.1, -0.09, 0.14, 0.06, 0.28, 0.35, and 0.5. The NDVI values were initially computed using the Natural Breaks (Jenks) method to classify NDVI map. Results confirmed that the area without vegetation, such as water bodies, as well as built up areas and barren lands, increased from 35 % in 2001 to 39.67 % in 2017.Key words: Normalized Difference Vegetation Index,land use/landcover, spectral signature 

scholarly journals LAND USE/LAND COVER CHANGE DETECTION USING MULTI–TEMPORAL SATELLITE DATASET: A CASE STUDY IN ISTANBUL NEW AIRPORT

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 17-22 ◽  
Author(s):  
D. Akyürek ◽  
Ö. Koç ◽  
E. M. Akbaba ◽  
F. Sunar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Environmental Changes ◽  
Satellite Image ◽  
The Black Sea ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Environmental Consequences ◽  
Transportation Capacity ◽  
Sentinel 2A

<p><strong>Abstract.</strong> In recent years, especially in metropolitan cities such as Istanbul, the emerging needs of the increasing population and demand for better air transportation capacity have led to big environmental changes. One of them is originated due to the construction of the new airport (Istanbul Grand Airport &amp;ndash; IGA), located on the Black Sea coast on the European side of Turkey and expected as “The biggest hub in Europe” by the early 2020s. The construction has five phases and first construction phase is scheduled to finish up by the end of 2018. With an advanced space technologies including remote sensing, environmental consequences due to Land Use/Land Cover changes (LULC) can be monitored and determined efficiently. The aim of this paper is to analyse LULC changes especially in the forest areas and water bodies by using two different satellite image dataset. In this context, supervised classification method and different spectral indices are applied to both Landsat-8 (2013&amp;ndash;2017) and Sentinel 2A (2015&amp;ndash;2017) image datasets to demonstrate the total and annual changes during the construction of the first phase. The efficiency of two datasets is outlined by comparison of the output thematic map accuracies.</p>

scholarly journals Land Use and Land Cover Dynamics and Assessing the Ecosystem Service Values in the Trans-Boundary Gandaki River Basin, Central Himalayas

2018 ◽  
Vol 10 (9) ◽  
pp. 3052 ◽  
Author(s):  
Raju Rai ◽  
Yili Zhang ◽  
Basanta Paudel ◽  
Bipin Acharya ◽  
Laxmi Basnet
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Cover ◽  
River Basin ◽  
Ecosystem Service ◽  
Satellite Image ◽  
Landsat 8 ◽  
Barren Land ◽  
Central Himalayas ◽  
Land Cover Types

Land use and land cover is a fundamental variable that affects many parts of social and physical environmental aspects. Land use and land cover changes (LUCC) has been known as one of the key drivers of affecting in ecosystem services. The trans-boundary Gandaki River Basin (GRB) is the part of Central Himalayas, a tributary of Ganges mega-river basin plays a crucial role on LUCC and ecosystem services. Due to the large topographic variances, the basin has existed various land cover types including cropland, forest cover, built-up area, river/lake, wetland, snow/glacier, grassland, barren land and bush/shrub. This study used Landsat 5-TM (1990), Landsat 8-OLI (2015) satellite image and existing national land cover database of Nepal of the year 1990 to analyze LUCC and impact on ecosystem service values between 1990 and 2015. Supervised classification with maximum likelihood algorithm was applied to obtain the various land cover types. To estimate the ecosystem services values, this study used coefficients values of ecosystem services delivered by each land cover class. The combined use of GIS and remote sensing analysis has revealed that grassland and snow cover decreased from 10.62% to 7.62% and 9.55% to 7.27%, respectively compared to other land cover types during the 25 years study period. Conversely, cropland, forest and built-up area have increased from 31.78% to 32.67%, 32.47–33.22% and 0.19–0.59%, respectively in the same period. The total ecosystem service values (ESV) was increased from 50.16 × 108 USD y−1 to 51.84 × 108 USD y−1 during the 25 years in the GRB. In terms of ESV of each of land cover types, the ESV of cropland, forest, water bodies, barren land were increased, whereas, the ESV of snow/glacier and grassland were decreased. The total ESV of grassland and snow/glacier cover were decreased from 3.12 × 108 USD y−1 to 1.93 × 108 USD y−1 and 0.26 × 108 USD y−1 to 0.19 × 108 USD y−1, respectively between 1990 and 2015. The findings of the study could be a scientific reference for the watershed management and policy formulation to the trans-boundary watershed.

CLASSIFICATION METHODS FOR REMOTELY SENSED DATA: LAND USE AND LAND COVER CLASSIFICATION USING VARIOUS COMBINATIONS OF BANDS

2015 ◽  
Vol 74 (10) ◽  
Author(s):  
Nur Anis Mahmon ◽  
Norsuzila Ya’acob ◽  
Azita Laily Yusof ◽  
Jasmee Jaafar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Ground Truth Data ◽  
Sensing Applications ◽  
Band Combinations

Land use and land cover (LU/LC) classification of remotely sensed data is an important field of research by which it is commonly used in remote sensing applications. In this study, the different types of classification techniques were compared using different RGB band combinations for classifying several satellite images of some parts of Selangor, Malaysia. For this objective, the classification was made using Landsat 8 satellite images and the Erdas Imagine software as the image processing package. From the classification output, the accuracy assessment and kappa statistic were evaluated to get the most accurate classifier. Optimal performance was identified by validating the classification results with ground truth data. From the results of the classified images, the Maximum Likelihood technique (overall accuracy 82.5%) was the highest and most applicable for satellite image classifications as compared with Mahalanobis Distance and Minimum Distance. Whereas for land use and land cover mapping, the RGB 4, 3, 2 band combinations were found to be more reliable. An accurate classification can produce a correct LU/LC map that can be used for various purposes.  

scholarly journals A INFLUÊNCIA DO USO E COBERTURA DA TERRA NOS PARÂMETROS DA QUALIDADE DA ÁGUA NA BACIA HIDROGRÁFICA DO RIO ABAÚNA, EM GETÚLIO VARGAS, RS

2017 ◽  
Vol 39 (2) ◽  
pp. 352 ◽  
Author(s):  
Cassia Figur ◽  
Janete Teresinha Reis
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Cover ◽  
Riparian Forest ◽  
Satellite Image ◽  
Quality Parameters ◽  
Human Consumption ◽  
Landsat 8 ◽  
Terrestrial Environment ◽  
Water Parameters

The water quality of a river basin dependent on numerous factors, whether natural or anthropogenic. Thus, this research aims to analyze the water quality parameters and the influence of land use and land cover in the watershed of Abaúna Rio, Brazil. The methodology made it possible to analyze the aquatic and terrestrial environment so as to achieve integration between the different uses and land cover and water parameters. For this, we used the satellite image Landsat 8, for that survey land use and land cover. The water parameters were: total suspended solids (TSS), water temperature (Temp.); hydrogen potential (pH); dissolved oxygen (DO), turbidity and total coliforms. From the integrated assessment of aquatic and terrestrial environment, it was found that water most collection points falls within the rules of CONAMA, but not for human consumption and it became clear that the uses are not in accordance with the rules of Federal Forest code. Therefore, the uses are interfering in water quality by altering the physical, chemical and bacteriological. For the improvement of the entire watershed water quality it is important to implement the use of alternative and land cover for the preservation and restoration of riparian forest.

scholarly journals Testing a detailed classification scheme for land-cover/ land-use mapping of typical Indonesian landscapes: case study of Sarolangun, Jambi and Salatiga, Central Java

2020 ◽  
Vol 52 (3) ◽  
pp. 327
Author(s):  
Projo Danoedoro ◽  
Irvan Nurrahman Ananda ◽  
Candra Sari Djati Kartika ◽  
Assyria F Umela ◽  
Alvidita Beatrix Indayani
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Development ◽  
Classification Scheme ◽  
Satellite Image ◽  
Digital Processing ◽  
Disaster Mitigation ◽  
Landsat 8 ◽  
Landscape Characteristics ◽  
Central Java

Land-cover/land-use (LCLU) mapping is an important activity to produce very useful information to support  various sectors, such as land supply, spatial planning, disaster mitigation, and agricultural development.  In Indonesia, a LCLU classification scheme has been developed at a scale of 1: 50,000, but it still requires an evaluation due to its advantages and limitations. This study tried to apply a classification scheme for LCLU-based on SNI 7645-1 2014 for two regions in Indonesia with different landscape characteristics, i.e.  Sarolangun in Jambi and Salatiga and surroundings in Central Java.. The trial was conducted by developing methods of Landsat-8 satellite image analysis and interpretation combining digital processing and manual delineation. Based on this research, a number of 52 LCLU classes were identified  in Sarolanguni and 32 classes were found in Salatiga and surrounding areas. The validation showed that the LCLU map of Jambi region reached 80.75.% of total accuracy, while that of Salatiga and surroundings reached 88.7%.  Different accuracies found related to the number of classes produced, the pattern of relationship between LCLU with the existing landform characteristics, and the quality of images due to cloud cover. 

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.

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