scholarly journals Application of Landsat Imagery and Vegetation Index Property to Assess the Shoreline Changes Along Cox’s Bazar–Teknaf Coast

2021 ◽  
Vol 12 (1) ◽  
pp. 21-28
Author(s):  
Umme Kulsum Navera ◽  
Md Safin Ahmed
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Sandy Beaches ◽  
Landsat Images ◽  
Sediment Distribution ◽  
Average Change ◽  
Severe Erosion ◽  
Induced Motion

Bangladesh is located at the head of the Bay of Bengal. The coast of Bangladesh is known as a zone of vulnerabilities as well as opportunities which involves coast and island boundaries. The eastern coastal zone consists of sandy beaches and hilly areas and is morphologically very dynamic. This shoreline is an important zone which facilitates tourism opportunity, fishing industry, natural resources and regional highway. Cox’s Bazar-Teknaf shoreline has been experiencing severe erosion at a number of places due to wave action. Wave and wind induced motion results in sediment distribution and shaping of nearshore morphology. The study has been performed by using Remote Sensing and GIS techniques. The shoreline shifting analysis has been performed by the process of open source Landsat images from 1980 to 2017. Satellite derived band algebra; Normalized Difference Vegetation Index has been utilized to identify the vegetation cover. The satellite images of an object carry a unique index property. In this study the index property of vegetation cover has been used to delineate more stable shorelines. At different locations, the average change in shoreline goes up to 120 m in erosion and 100 m in deposition. Based on the coastline shifting the erosion behaviour and the vulnerable areas are identified. Journal of Engineering Science 12(1), 2021, 21-28

scholarly journals Spatiotemporal Analysis of Vegetation Cover Change in a Large Ephemeral River: Multi-Sensor Fusion of Unmanned Aerial Vehicle (UAV) and Landsat Imagery

2020 ◽  
Vol 13 (1) ◽  
pp. 51
Author(s):  
Bryn E. Morgan ◽  
Jonathan W. Chipman ◽  
Douglas T. Bolger ◽  
James T. Dietrich
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Temporal Trends ◽  
Landsat 8 ◽  
Landsat Images ◽  
Fractional Vegetation Cover ◽  
Aerial Vehicle

Ephemeral rivers in arid regions act as linear oases, where corridors of vegetation supported by accessible groundwater and intermittent surface flows provide biological refugia in water-limited landscapes. The ecological and hydrological dynamics of these systems are poorly understood compared to perennial systems and subject to wide variation over space and time. This study used imagery obtained from an unmanned aerial vehicle (UAV) to enhance satellite data, which were then used to quantify change in woody vegetation cover along the ephemeral Kuiseb River in the Namib Desert over a 35-year period. Ultra-high resolution UAV imagery collected in 2016 was used to derive a model of fractional vegetation cover from five spectral vegetation indices, calculated from a contemporaneous Landsat 8 Operational Land Imager (OLI) image. The Normalized Difference Vegetation Index (NDVI) provided the linear best-fit relationship for calculating fractional cover; the model derived from the two 2016 datasets was subsequently applied to 24 intercalibrated Landsat images to calculate fractional vegetation cover for the Kuiseb extending back to 1984. Overall vegetation cover increased by 33% between 1984 and 2019, with the most highly vegetated reach of the river exhibiting the greatest positive change. This reach corresponds with the terminal alluvial zone, where most flood deposition occurs. The spatial and temporal trends discovered highlight the need for long-term monitoring of ephemeral ecosystems and demonstrate the efficacy of a multi-sensor approach to time series analysis using a UAV platform.

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 Modelling and Delineation of Hydrocarbon Micro - Seepage Prone Zones on Soil and Sediment in Ugwueme, South Eastern Nigeria with Soil Adjustment Vegetation Index (SAVI)

2020 ◽  
pp. 13-33
Author(s):  
Mfoniso Asuquo Enoh ◽  
Uzoma Chinenye Okeke ◽  
Needam Yiinu Barinua
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Landsat 8 ◽  
Landsat Images ◽  
Landsat 7 ◽  
Soil And Sediment

Remote Sensing is an excellent tool in monitoring, mapping and interpreting areas, associated with hydrocarbon micro-seepage. An important technique in remote sensing known as the Soil Adjusted Vegetation Index (SAVI), adopted in many studies is often used to minimize the effect of brightness reflectance in the Normalized Difference Vegetation Index (NDVI), related with soil in areas of spare vegetation cover, and mostly in areas of arid and semi–arid regions. The study aim at analyzing the effect of hydrocarbon micro – seepage on soil and sediments in Ugwueme, Southern Eastern Nigeria, with SAVI image classification method. To achieve this aim, three cloud free Landsat images, of Landsat 7 TM 1996 and ETM+ 2006 and Landsat 8 OLI 2016 were utilized to produce different SAVI image classification maps for the study.  The SAVI image classification analysis for the study showed three classes viz Low class cover, Moderate class cover and high class cover.  The category of high SAVI density classification was observed to increase progressive from 31.95% in 1996 to 34.92% in 2006 and then to 36.77% in 2016. Moderately SAVI density classification reduced from 40.53% in 1996 to 38.77% in 2006 and then to 36.96% in 2016 while Low SAVI density classification decrease progressive from 27.51% in 1996 to 26.31% in 2006 and then increased to 28.26% in 2016. The SAVI model is categorized into three classes viz increase, decrease and unchanged. The un – changed category increased from 12.32km2 (15.06%) in 1996 to 17.17 km2 (20.96%) in 2006 and then decelerate to 13.50 km2 (16.51%) in 2016.  The decrease category changed from 39.89km2 (48.78%) in 1996 to 40.45 km2 (49.45%) in 2006 and to 51.52 km2 (63.0%) in 2016 while the increase category changed from 29.57km2 (36.16%) in 1996 to 24.18 km2 (29.58%) in 2006 and to 16.75 km2 (20.49%) in 2016. Image differencing, cross tabulation and overlay operations were some of the techniques performed in the study, to ascertain the effect of hydrocarbon micro - seepage.  The Markov chain analysis was adopted to model and predict the effect of the hydrocarbon micro - seepage for the study for 2030.  The study expound that the SAVI is an effective technique in remote sensing to identify, map and model the effect of hydrocarbon micro - seepage on soil and sediment particularly in areas characterized with low vegetation cover and bare soil cover.

scholarly journals Impacts of tropical hurricanes on the vegetation cover of the lower basin and estuary of San José del Cabo, Baja California Sur, Mexico

2020 ◽  
Vol 27 (1) ◽  
pp. 165-180
Author(s):  
Marcos Shiba-Reyes ◽  
◽  
Enrique Troyo ◽  
Raúl Martínez-Rincón ◽  
Aurora Breceda ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Type ◽  
Stability Loss ◽  
San Jose ◽  
Landsat Images ◽  
Precipitation Decrease ◽  
One Year ◽  
The Impact

Introduction: Tropical hurricanes modify composition and structure of ecosystems. Objective: To analyze the impact of tropical hurricanes on the recovery and resilience of vegetation cover.Materials and methods: The resilience of the lower basin and estuary of San Jose del Cabo was evaluated by studying the impact of 11 tropical hurricanes (2013-2017) on the vegetation cover. Landsat images were analyzed for each event and two SPOT-6 images for the Hurricane Lidia. The areas of gain, stability, loss and recovery of vegetation types were estimated based on the analysis of changes in the Normalized Difference Vegetation Index (NDVI).Results and discussion: Average stability of vegetation cover was 90 %; however, in the case of hurricane Odile (2014) and Lidia (2017), stability decreased considerably, with a loss of 35.4 and 20.5 %, respectively, being the perennial herbaceous vegetation the most affected. One year after Odile and Lidia, recovery was 8.4 % and 25.4 %, respectively; the most recovered vegetation type was reed-tree. The analysis of SPOT-6 images allowed the detailed observation of Lidia's effect on palm grove. The main cause of its loss was runoff from the stream, which favored the growth of invasive species (Arundo donax L. and Tamarix sp.); furthermore, it was estimated that 1.4 ha were deforested, and an area of 20 ha affected by fire in 2017.Conclusion: Vegetation is resilient to tropical hurricanes; however, events that provide more than 50 % of annual precipitation decrease the capacity of vegetation to recover.

scholarly journals Urban Fire Severity and Vegetation Dynamics in Southern California

2020 ◽  
Vol 13 (1) ◽  
pp. 19
Author(s):  
Lauren E. H. Mathews ◽  
Alicia M. Kinoshita
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Current Knowledge ◽  
Fire Severity ◽  
Satellite Image ◽  
Burn Severity ◽  
Native Vegetation ◽  
Riparian Areas ◽  
The Impact

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

Change on detection of vegetation cover and soil salinity using GIS technique in Diyala Governorate, Iraq

2021 ◽  
Vol 30 (1) ◽  
pp. 148-158
Author(s):  
Haneen Adeeb ◽  
Yaseen Al-Timimi
Keyword(s):  
Soil Salinity ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Inverse Correlation ◽  
Salinity Changes ◽  
Gis Technique ◽  
Salinity Index ◽  
Remote Sensing Techniques ◽  
Strong Inverse Correlation

Soil salinity is one of the most important problems of land degradation, that threatening the environmental, economic and social system. The aim of this study to detect the changes in soil salinity and vegetation cover for Diyala Governorate over the period from 2005 to 2020, through the use of remote sensing techniques and geographic information system. The normalized difference vegetation index (NDVI) and salinity index (SI) were used, which were applied to four of the Landsat ETM+ and Landsat OLI satellite imagery. The results showed an increase in soil salinity from 7.27% in the period 2005–2010 to 27.03% in 2015–2020, as well as an increase in vegetation from 10% to 24% in the same period. Also the strong inverse correlation between the NDVI and the SI showed that vegetation is significantly affected and directly influenced by soil salinity changes

scholarly journals Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

2018 ◽  
Vol 7 (4) ◽  
pp. 297-306 ◽  
Author(s):  
Amal Y. Aldhebiani ◽  
Mohamed Elhag ◽  
Ahmad K. Hegazy ◽  
Hanaa K. Galal ◽  
Norah S. Mufareh
Keyword(s):  
Saudi Arabia ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Floristic Composition ◽  
Water Source ◽  
Shannon Index ◽  
Landsat 8 ◽  
Main Stream

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

scholarly journals A Study of Desertification Using Remote Sensing Techniques in Basra Governorate, South Iraq

2021 ◽  
pp. 912-926
Author(s):  
Fadel Abbas Zwain ◽  
Thair Thamer Al-Samarrai ◽  
Younus I. Al-Saady
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arid Zone ◽  
Sand Dunes ◽  
Risk Index ◽  
Anthropogenic Factors ◽  
Landsat Images

Iraq territory as a whole and south of Iraq in particular encountered rapid desertification and signs of severe land degradation in the last decades. Both natural and anthropogenic factors are responsible for the extent of desertification. Remote sensing data and image analysis tools were employed to identify, detect, and monitor desertification in Basra governorate. Different remote sensing indicators and image indices were applied in order to better identify the desertification development in the study area, including the Normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), Salinity index (SI), Top Soil Grain Size Index (GSI) , Land Surface Temperature (LST) , Land Surface Soil Moisture (LSM), and Land Degradation Risk Index (LDI) which was used for the assessment of degradation severity .Three Landsat images, acquired in 1973, 1993, and 2013, were used to evaluate the potential of using remote sensing analysis in desertification monitoring. The approach applied in this study for evaluating this phenomenon was proven to be an effective tool for the recognition of areas at risk of desertification. The results indicated that the arid zone of Basra governorate encounters substantial changes in the environment, such as decreasing surface water, degradation of agricultural lands (as palm orchards and crops), and deterioration of marshlands. Additional changes include increased salinization with the creeping of sand dunes to agricultural areas, as well as the impacts of oil fields and other facilities.

scholarly journals Heat Islands in the city of San Francisco de Campeche: Detection and solution

2020 ◽  
pp. 16-20
Author(s):  
Román Alejandro Canul-Turriza ◽  
Francisco Javier Barrera-Lao ◽  
Gabriela Patricia Aldana Narváez
Keyword(s):  
Soil Temperature ◽  
San Francisco ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Islands ◽  
Mathematical Algorithm ◽  
Green Areas ◽  
Landsat 5 Tm ◽  
The City

This paper presents the identification of heat islands in the city of San Francisco de Campeche, period 1990 - 2020 and their relationship with changes in the vegetation cover areas. To identify the heat islands in the city, 6 Landsat 5 (TM), 7 (TM) and 8 (OIL) images were obtained from the USGS database (http://earthexplorer.usgs.gov/). In geographic information software, soil temperature was calculated from a mathematical algorithm applied to thermal infrared bands 6 and 10, in addition, the Normalized Difference Vegetation Index (NDVI) was calculated, in order to find a relationship between changes in temperature and vegetation cover. It was found that the green areas have reduced their surface by more than 50% and the soil temperature has increased up to 7 ° C

scholarly journals Monitoring of Inland Surface Water Quality Using Remote Sensing on the Example of Wigry Lake

2018 ◽  
Vol 63 ◽  
pp. 00017
Author(s):  
Michał Lupa ◽  
Katarzyna Adamek ◽  
Renata Stypień ◽  
Wojciech Sarlej
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Water Quality ◽  
Lake Area ◽  
Landsat Images ◽  
Secchi Disc ◽  
Water Index ◽  
Normalized Difference Water Index

The study examines how LANDSAT images can be used to monitor inland surface water quality effectively by using correlations between various indicators. Wigry lake (area 21.7 km2) was selected for the study as an example. The study uses images acquired in the years 1990–2016. Analysis was performed on data from 35 months and seven water condition indicators were analyzed: turbidity, Secchi disc depth, Dissolved Organic Material (DOM), chlorophyll-a, Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The analysis of results also took into consideration the main relationships described by the water circulation cycle. Based on the analysis of all indicators, clear trends describing a systematic improvement of water quality in Lake Wigry were observed.

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