scholarly journals Rain-Fed Wheat Area Mapping Using MODIS and Landsat Images (Case Study: Ahar City)

2021 ◽  
Vol 12 (4) ◽  
pp. 19-34
Author(s):  
Amir Hossen Nazemi ◽  
Hamed Sabzchi ◽  
Aliashrafi Sadraddini ◽  
Abolfazl Majnooni Haris
Keyword(s):  
Landsat Images

Integration of Satellite Remote Sensing Techniques and Landscape Metrics to Characterize Land Cover Change and Dynamics

2013 ◽  
pp. 228-244
Author(s):  
Carmelo Riccardo Fichera ◽  
Giuseppe Modica ◽  
Maurizio Pollino
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Landscape Metrics ◽  
Landsat Images ◽  
Aerial Photos ◽  
Multi Temporal ◽  
Urban Rural ◽  
Remote Sensing Techniques

One of the most relevant applications of Remote Sensing (RS) techniques is related to the analysis and the characterization of Land Cover (LC) and its change, very useful to efficiently undertake land planning and management policies. Here, a case study is described, conducted in the area of Avellino (Southern Italy) by means of RS in combination with GIS and landscape metrics. A multi-temporal dataset of RS imagery has been used: aerial photos (1954, 1974, 1990), Landsat images (MSS 1975, TM 1985 and 1993, ETM+ 2004), and digital orthophotos (1994 and 2006). To characterize the dynamics of changes during a fifty year period (1954-2004), the approach has integrated temporal trend analysis and landscape metrics, focusing on the urban-rural gradient. Aerial photos and satellite images have been classified to obtain maps of LC changes, for fixed intervals: 1954-1985 and 1985-2004. LC pattern and its change are linked to both natural and social processes, whose driving role has been clearly demonstrated in the case analysed. In fact, after the disastrous Irpinia earthquake (1980), the local specific zoning laws and urban plans have significantly addressed landscape changes.

scholarly journals Improving estimation of glacier volume change: a GLIMS case study of Bering Glacier System, Alaska

2008 ◽  
Vol 2 (1) ◽  
pp. 33-51 ◽  
Author(s):  
M. J. Beedle ◽  
M. Dyurgerov ◽  
W. Tangborn ◽  
S. J. S. Khalsa ◽  
C. Helm ◽  
...  
Keyword(s):  
Volume Change ◽  
Balance Model ◽  
Drainage Basins ◽  
Landsat Images ◽  
Glacier System ◽  
Digital Elevation ◽  
Bering Glacier ◽  
Elevation Model ◽  
Dependent Mass

Abstract. The Global Land Ice Measurements from Space (GLIMS) project has developed tools and methods that can be employed by analysts to create accurate glacier outlines. To illustrate the importance of accurate glacier outlines and the effectiveness of GLIMS standards we conducted a case study on Bering Glacier System (BGS), Alaska. BGS is a complex glacier system aggregated from multiple drainage basins, numerous tributaries, and many accumulation areas. Published measurements of BGS surface area vary from 1740 to 6200 km2, depending on how the boundaries of this system have been defined. Utilizing GLIMS tools and standards we have completed a new outline (3630 km2) and analysis of the area-altitude distribution (hypsometry) of BGS using Landsat images from 2000 and 2001 and a US Geological Survey 15-min digital elevation model. We compared this new hypsometry with three different hypsometries to illustrate the errors that result from the widely varying estimates of BGS extent. The use of different BGS hypsometries results in highly variable measures of volume change and net balance (bn). Applying a simple hypsometry-dependent mass-balance model to different hypsometries results in a bn rate range of −1.0 to −3.1 m a−1 water equivalent (W.E.), a volume change range of −3.8 to −6.7 km3 a−1 W.E., and a near doubling in contributions to sea level equivalent, 0.011 mm a−1 to 0.019 mm a−1. Current inaccuracies in glacier outlines hinder our ability to correctly quantify glacier change. Understanding of glacier extents can become comprehensive and accurate. Such accuracy is possible with the increasing volume of satellite imagery of glacierized regions, recent advances in tools and standards, and dedication to this important task.

scholarly journals Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

2021 ◽  
Vol 10 (7) ◽  
pp. 475
Author(s):  
Ting Zhang ◽  
Ruiqing Yang ◽  
Yibo Yang ◽  
Long Li ◽  
Longqian Chen
Keyword(s):  
Remote Sensing ◽  
Quality Assessment ◽  
Urban Expansion ◽  
Assessment Method ◽  
Landsat Images ◽  
Ecological Index ◽  
Environment Quality ◽  
Quality Assessment Method ◽  
The Impact

The remote-sensing ecological index (RSEI), which is built with greenness, moisture, dryness, and heat, has become increasingly recognized for its use in urban eco-environment quality assessment. To improve the reliability of such assessment, we propose a new RSEI-based urban eco-environment quality assessment method where the impact of RSEI indicators on the eco-environment quality and the seasonal change of RSEI are examined and considered. The northern Chinese municipal city of Tianjin was selected as a case study to test the proposed method. Landsat images acquired in spring, summer, autumn, and winter were obtained and processed for three different years (1992, 2005, and 2018) for a multitemporal analysis. Results from the case study show that both the contributions of RSEI indicators to eco-environment quality and RSEI values vary with the season and that such seasonal variability should be considered by normalizing indicator measures differently and using more representative remote-sensing images, respectively. The assessed eco-environment quality of Tianjin was, overall, improving owing to governmental environmental protection measures, but the damage caused by rapid urban expansion and sea reclamation in the Binhai New Area still needs to be noted. It is concluded that our proposed urban eco-environment quality assessment method is viable and can provide a reliable assessment result that helps gain a more accurate understanding of the evolution of the urban eco-environment quality over seasons and years.

scholarly journals Fractional Vegetation Cover Change Detection In Megacities Using Landsat Time-Series Images: A Case Study Of Hanoi City (Vietnam) During 1986-2019

2019 ◽  
Vol 12 (4) ◽  
pp. 175-187
Author(s):  
Thanh Tien Nguyen
Keyword(s):  
Rural Areas ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification Method ◽  
Landsat 8 ◽  
Landsat Images ◽  
Fractional Vegetation Cover ◽  
Multi Temporal

The objective of the study is to assess changes of fractional vegetation cover (FVC) in Hanoi megacity in period of 33 years from 1986 to 2016 based on a two endmember spectral mixture analysis (SMA) model using multi-spectral and multi-temporal Landsat-5 TM and -8 OLI images. Landsat TM/OLI images were first radiometrically corrected. FVC was then estimated by means of a combination of Normalized Difference Vegetation Index (NDVI) and classification method. The estimated FVC results were validated using the field survey data. The assessment of FVC changes was finally carried out using spatial analysis in GIS. A case study from Hanoi city shows that: (i) the proposed approach performed well in estimating the FVC retrieved from the Landsat-8 OLI data and had good consistency with in situ measurements with the statistically achieved root mean square error (RMSE) of 0.02 (R 2 =0.935); (ii) total FVC area of 321.6 km 2 (accounting for 9.61% of the total area) was slightly reduced in the center of the city, whereas, FVC increased markedly with an area of 1163.6 km 2 (accounting for 34.78% of the total area) in suburban and rural areas. The results from this study demonstrate the combination of NDVI and classification method using Landsat images are promising for assessing FVC change in megacities.

scholarly journals MONITORING THE RATE OF EXPANSION OF AGRICULTURAL FIELDS IN MWEKERA FOREST RESERVE USING REMOTE SENSING AND GIS

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 377-380
Author(s):  
K. Kanja ◽  
M. Mwemba ◽  
K. Malunga
Keyword(s):  
Remote Sensing ◽  
Accuracy Assessment ◽  
Remote Sensing And Gis ◽  
Agricultural Fields ◽  
Urban Migration ◽  
Landsat Images ◽  
Forest Reserve ◽  
Job Opportunities ◽  
Analysis Technique

<p><strong>Abstract.</strong> Rapid population growth and rural-urban migration amidst limited job opportunities lead to conversion of land from forests into agriculture and other land uses. In this study, Zambia’s Mwekera national forest reserve was used as a case study to assess the rate of expansion of agricultural fields using remote sensing and GIS. Iterative Self-Organizing Data Analysis Technique (ISODATA) as well as maximum likelihood supervised classification on four Landsat images as well as accuracy assessment of the classifications was performed. Over the period under observation, results indicate annual percentage changes to be &amp;minus;0.03, &amp;minus;0.49 and 1.26 for agriculture, forests and settlement respectively indicating a higher conversion of forests into human settlements and agriculture.</p>

scholarly journals FLOOD MONITORING USING NDWI AND MNDWI SPECTRAL INDICES: A CASE STUDY OF AGHQALA FLOOD-2019, GOLESTAN PROVINCE, IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 605-607
Author(s):  
F. Khalifeh Soltanian ◽  
M. Abbasi ◽  
H. R. Riyahi Bakhtyari
Keyword(s):  
Natural Hazards ◽  
Satellite Imagery ◽  
Water Bodies ◽  
Detection Methods ◽  
Spectral Indices ◽  
Landsat Images ◽  
Flood Monitoring ◽  
Golestan Province ◽  
Digital Methods

Abstract. Assessment of changes of water bodies and vegetation by traditional methods is very difficult and costly. The use of satellite data makes it possible to study water bodies and vegetation more accurately and cost effectively. Accordingly, various digital methods have been developed to discover and detect changes of earth's surface features. Flood is one of the important factors contributing to the destruction of natural resources. The purpose of this research is to evaluate the flood areas in the Aghqala area in Golestan province of Iran. The level of water bodies in the spring of 2018 and 2019 was compared and evaluated based on the NDWI and MNDWI indices using Landsat images. The results showed that water bodies’ area in the spring of 2018 was 24.13 km2 which increased to 185.34 km2 at 2019 using NDWI; while the MNDWI due to the excessive sensitivity to the water considered agriculture wetlands as an area of water bodies. Therefore, the NDWI yielded more logical results. Also, change detection methods based on spectral and radiometric information using indices are more accurate than the classification maps and more changes can be shown. Using satellite imagery to monitor changes is essential to facilitate the planning of natural hazards management.

scholarly journals A simple, robust, and automatic approach to extract water body from Landsat images (case study: Lake Urmia, Iran)

2019 ◽  
Author(s):  
Hadiseh Babaei ◽  
Milad Janalipour ◽  
Nadia Abbaszadeh Tehrani
Keyword(s):  
Saline Lakes ◽  
Continuous Monitoring ◽  
Landsat 8 ◽  
Lake Area ◽  
Spectral Indices ◽  
Lake Urmia ◽  
Landsat Images ◽  
The World ◽  
Study Lake

Abstract Lake Urmia is one of the largest saline lakes in the world, and has a great effect on its surrounding ecosystems as well as the economic, social, and even cultural condition of its basin inhabitants. Hence, continuous monitoring of lake area changes is necessary and unavoidable for better land management and prevention of its degradation. In this study, by using Landsat 8 images and by preforming some essential pre-processing tasks, the area of the lake was estimated using the number of traditional spectral indices and a new one and the automatic Otsu's thresholding method for 5 years (2013–2017). The results showed that this index shows more accurate results than other indices when estimating the area of the lake and can separate water class from land one with an average overall accuracy of 96%.

scholarly journals Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations

2019 ◽  
Vol 11 (11) ◽  
pp. 1305 ◽  
Author(s):  
Eduardo R. Oliveira ◽  
Leonardo Disperati ◽  
Luca Cenci ◽  
Luísa Gomes Pereira ◽  
Fátima L. Alves
Keyword(s):  
Change Detection ◽  
Satellite Remote Sensing ◽  
Detection Method ◽  
Remote Sensing Data ◽  
Common Condition ◽  
Landsat Images ◽  
Multi Index ◽  
Image Differencing ◽  
Detection Approach

Satellite remote sensing data are often used to extract water surfaces related to extreme events like floods. This study presents the Multi INDEx Differencing (MINDED) method, an innovative procedure to estimate flood extents, aiming at improving the robustness of single water-related indices and threshold-based approaches. MINDED consists of a change detection approach integrating specific sensitivities of several indices. Moreover, the method also allows to quantify the uncertainty of the Overall flood map, based on both the agreement level of the stack of classifications and the weight of every index obtained from the literature. Assuming the lack of ground truths to be the most common condition in flood mapping, MINDED also integrates a procedure to reduce the subjectivity of thresholds extraction focused on the analysis of water-related indices frequency distribution. The results of the MINDED application to a case study using Landsat images are compared with an alternative change detection method using Sentinel-1A data, and demonstrate consistency with local fluvial flood records.

scholarly journals Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China

2015 ◽  
Vol 47 ◽  
pp. 183-195 ◽  
Author(s):  
Shougeng Hu ◽  
Luyi Tong ◽  
Amy E. Frazier ◽  
Yansui Liu
Keyword(s):  
Boundary Extraction ◽  
Landsat Images
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