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.

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 Responses of Spatiotemporal Vegetative Land Cover To Meteorological Changes in Bangladesh

2021 ◽  
Author(s):  
Md. Abdul Fattah ◽  
Syed Riad Morshed
Keyword(s):  
Regression Models ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Climatic Variables ◽  
Landsat Images ◽  
Natural Landscape ◽  
Impact Of Urbanization ◽  
Rainfall Statistics ◽  
The Impact

Abstract Quantifying the response of vegetation cover change (VCC) to climatic variables is a gap that is mandatory for the conservation and rehabilitation of natural landscape to ensure sustainability. This study aims to assess the response of VCC to temperature and rainfall change in Bangladesh. We used (i) Landsat images to analyze VCC using image classification method, Normalized Difference Vegetation Index (NDVI) (ii) temperature and rainfall statistics to investigate the spatiotemporal variations (SV) of meteorological factors, urban lands, VCC in all the 64 districts of Bangladesh during 1990-2018 and examined their correlation. To quantify the impact of urbanization on VCC, two regression models were built between growing-season NDVI (GNDVI) and urban land proportion (PLU). Results show that the SV of precipitation, temperature, GNDVI, and PUL varied greatly among the districts. GNDVI was found closely related to climatic variables and less sensitive to climatic factor changes. There has been found a significant correlation between the trend of GNDVI and GP while the negative correlation between GNDVI trend and GT, ΔPUL. Strong sensitivity of GNDVI change to GP was calculated in the range of precipitation 2200-3000mm and GNDVI to GT change in the range of temperature 300C-310C. Besides, urban expansion was found mostly responsible for VCC in the study area.

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 FIRE SEVERITY ASSESSMENT USING NDVI DERIVED FROM LANDSAT TM/ETM IMAGES AND TERRAIN DATA

2017 ◽  
pp. 29-37 ◽  
Author(s):  
Ibrahim Molla ◽  
Emiliya Velizarova ◽  
Mariana Zaharinova
Keyword(s):  
Forest Fires ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fire Severity ◽  
Landsat 8 ◽  
Fire Occurrence ◽  
One Year ◽  
In Fire

The forest fires influence on the plants and soil depends on the fire severity and time of exposure. Fire severity integrates physical, chemical and biological changes occurring in ecosystems on the area as a consequence of fire influence. The purpose of the current investigation was to examine the role of the forest fire severity on the vegetation cover of the area of Svilengrad Municipality, using NDVI (Normalized Difference Vegetation Index) before fire and after fire, derived from LANDSAT 8 TM/ETM images. The comparison of the data from NDVI and that observed on the terrain data was also targeted. The results show that NDVI are changed significantly in fire affected area depending on vegetation cover and type of fire. This index also is very sensitive to changes during time after fire occurrence. One year after fire occurrence the NDVI values increased to +0.305 (0.048) for whole studied area. Through dNDVI could be distinguish the recovery rates of the fire affected areas with different tree species.

scholarly journals Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

2018 ◽  
Vol 10 (12) ◽  
pp. 2034 ◽  
Author(s):  
Zengjing Song ◽  
Ruihai Li ◽  
Ruiyang Qiu ◽  
Siyao Liu ◽  
Chao Tan ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Air Pollutant ◽  
The Arctic ◽  
Composition Analysis ◽  
The Impact

Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 μg/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72°N and 48°S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature.

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 Analysis of Land Cover Changing and Vegetation Index at Kuranji Watershed in Padang, West Sumatera, Indonesia

2019 ◽  
Vol 4 (4) ◽  
pp. 286-290
Author(s):  
Rifky Putera ◽  
Junaidi Junaidi ◽  
Ahmad Junaidi
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Density ◽  
Landsat Images ◽  
Sensing Technology ◽  
One Year ◽  
Dense Class ◽  
Topographic Condition

Various activities around Kuranji watershed included the land conversioncan be impacted to topographic condition and also contributed to altering the vegetation density. Remote sensing technology is an effective methodfor land cover mapping. The objectives of the present study were to analyze the changing of land cover and classifying the vegetation density index in the upstream Kuranji Watershed. This study was conducted at Kuranji Watershed in Padang, West Sumatera Province. Two Landsat images representing the changing of the watershed area during 2017 and 2018 as well as obtaining the classification of vegetation density during corresponding years.Landsat 8 OLI images were classified using a supervised classification technique, then computed the vegetation index using the Normalized Difference Vegetation Index (NDVI). The result showed that the extension of forest area, settlement area and paddy field (283.92; 35.06; and 27 Ha, respectively) and decline of mix dryland agriculture, shrub and garden area (93.68; 277.43; and 190.95 Ha respectively). Decreasing of dense vegetation found at lower dense class (6.47 Ha) and highest dense class (5535.35 Ha). Therefore, the increasing area found at the cloud, dense and higher dense class (93.17; 5525.1; and 109.94 Ha, respectively). So, it is highlighted that changing land cover and vegetation index happen during the only one-year period.

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 Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

2020 ◽  
Vol 12 (2) ◽  
pp. 220 ◽  
Author(s):  
Han Xiao ◽  
Fenzhen Su ◽  
Dongjie Fu ◽  
Qi Wang ◽  
Chong Huang
Keyword(s):  
Spatial Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mangrove Ecosystem ◽  
Early Growth ◽  
Growth Stages ◽  
Human Disturbances ◽  
Vegetation Growth ◽  
Two Parameters ◽  
The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

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