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 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 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 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.

Measuring UHI using Landsat 8 OLI and TIRS data with NDVI and NDBI in Municipality of Prishtina

2021 ◽  
Vol 14 (11) ◽  
pp. 25-36
Author(s):  
Florim Isufi ◽  
Albert Berila ◽  
Shpejtim Bulliqi
Keyword(s):  
Regression Analysis ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Strong Positive Correlation ◽  
Strong Negative Correlation ◽  
Urban Heat ◽  
Surface Urban Heat Island

The study is aimed at investigating the phenomenon of the Surface Urban Heat Island (SUHI) over the municipality of Prishtina. The SUHI was investigated based on the relationship between Land Surface Temperature (LST) estimated from Landsat 8 Thermal Infrared Sensor (TIRS) band with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) from Landsat 8 Operational Land Imager (OLI) bands using Geographic Information System (GIS). To understand this relationship, a regression analysis was performed. Regression analysis in both cases showed high relationships between LST, NDVI and NDBI. LST relationships with NDVI showed a strong negative correlation having an R2 value of 0.7638 highlighting the extraordinary role of vegetation towards reducing the SUHI effect while LST relationships with NDBI showed a strong positive correlation having an R2 value of 0.8038 highlighting the role that built-up areas have in strengthening the SUHI effect. Built-up areas and bare surfaces are responsible for generating the SUHI effect while vegetation and water bodies minimize this effect by creating freshness. The maps in which the SUHI phenomenon are identified, are extremely important and should be paid great attention by the city leaders themselves. This should be done in order for urban planning policies to go to those areas where such a harmful phenomenon occurs in order for the lives of citizens to be as healthy as possible.

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 Analysis of vegetation dynamics using the normalized difference vegetation index (NDVI) at the archipelago of Fernando de Noronha, Pernambuco, Brazil

2020 ◽  
pp. 885-901
Author(s):  
Kardelan Arteiro da Silva ◽  
Soraya Giovanetti El-Deir ◽  
José Jorge Monteiro Júnior ◽  
João Paulo de Oliveira Santos ◽  
Emanuel Araújo Silva
Keyword(s):  
State Government ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Plan ◽  
Landsat 8 ◽  
Conservation Policies ◽  
Management Plans ◽  
Landsat 7 ◽  
Fernando De Noronha

Island environments have specific biotic and abiotic characteristics, as fragility, limitation of natural resources, geographic isolation, and fragmentation are determining factors that directly affect these areas. Thus, it is relevant to understand the natural evolution of the landscape in the islands, considering the anthropic actions and climate changes in the transformation of vegetation cover, as a means of time series and study of satellite images. This paper aims to analyze the dynamics of the landscape (changes in vegetation cover) of the Fernando de Noronha Archipelago concerning urban development, and other anthropic activities that occurred between 1999 and 2018, through remote sensing images, to establish comparisons with the Island Management Plans that were elaborated in the years of 2005 and 2017. Also, this study intends to raise elements to assist in the spatial management of the Archipelago and to establish Public Conservation Policies for Fernando de Noronha and other island areas. Images from Landsat 7 and Landsat 8 were obtained for scenes from 1999 and 2017, respectively. These images were preprocessed and analyzed in Quantum GIS 2.18 software. And applied the NDVI calculation. It was also used the database found in the sustainable management plan of the archipelago provided by the state government of Pernambuco. With these data, it was possible to diagnose a vegetative growth on the island of about 45.36% in 17 years corroborating with the changes found in the data coming from the island's management plan. However, there are no changes in the phytosociological diversity of the island, this cause is pointed out to the invading and ruderals species of the island that are established and propagate.

scholarly journals Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

2018 ◽  
Vol 10 (10) ◽  
pp. 1614 ◽  
Author(s):  
Haishuo Wei ◽  
Juanle Wang ◽  
Kai Cheng ◽  
Ge Li ◽  
Altansukh Ochir ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Feature Space ◽  
Gobi Desert ◽  
Landsat 8 ◽  
Mongolian Plateau ◽  
Size Index ◽  
Modified Soil

The Mongolian plateau is a hotspot of global desertification because it is heavily affected by climate change, and has a large diversity of vegetation cover across various regions and seasons. Within this arid region, it is difficult to distinguish desertified land from other land cover types using low-quality vegetation information. To address this, we analyze both the effects and the applicability of different feature space models for the extraction of desertification information with the goal of finding appropriate approaches to extract desertification data on the Mongolian plateau. First, we used Landsat 8 remote sensing images to invert NDVI (normalized difference vegetation index), MSAVI (modified soil adjusted vegetation index), TGSI (topsoil grain size index), and albedo (land surface albedo) data. Then, we constructed the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, and compared their extraction accuracies. Our results show that the overall classification accuracies of the three models were 84.53%, 85.60%, and 88.27%, respectively, indicating that the three feature space models are feasible for extracting information relating to desertification on the Mongolian plateau. Further analysis indicates that the Albedo-NDVI model is suitable for areas with a high vegetation cover or a high forest ratio, whilst the Albedo-MSAVI model is suitable for areas with relatively low vegetation cover, and the Albedo-TGSI model is suitable for areas with extremely low vegetation cover, including the widely distributed Gobi Desert and other barren areas. This study provides a technical selection reference for the investigation of desertification of different zones on the Mongolian plateau.

scholarly journals A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data

Environments ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 36 ◽  
Author(s):  
Ana Teodoro ◽  
Ana Amaral
Keyword(s):  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Burn Severity ◽  
Landsat 8 ◽  
Fire Event ◽  
Burned Areas ◽  
Area Burned ◽  
Sentinel 2A ◽  
Normalized Burn Ratio

Forest areas in Portugal are often affected by fires. The objective of this work was to analyze the most fire-affected areas in Portugal in the summer of 2016 for two municipalities considering data from Landsat 8 OLI and Sentinel 2A MSI (prefire and postfire data). Different remote sensed data-derived indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), could be used to identify burnt areas and estimate the burn severity. In this work, NDVI was used to evaluate the area burned, and NBR was used to estimate the burn severity. The results showed that the NDVI decreased considerably after the fire event (2017 images), indicating a substantial decrease in the photosynthesis activity in these areas. The results also indicate that the NDVI differences (dNDVI) assumes the highest values in the burned areas. The results achieved for both sensors regarding the area burned presented differences from the field data no higher than 13.3% (for Sentinel 2A, less than 7.8%). We conclude that the area burned estimated using the Sentinel 2A data is more accurate, which can be justified by the higher spatial resolution of this data.

scholarly journals Evaluation of Optical Remote Sensing Data in Burned Areas Mapping of Thasos Island, Greece

2020 ◽  
Vol 4 (4) ◽  
pp. 813-826
Author(s):  
Mohamed Elhag ◽  
Nese Yimaz ◽  
Jarbou Bahrawi ◽  
Silvena Boteva
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Mediterranean Forests ◽  
Burned Areas ◽  
Multi Temporal

AbstractForest fires are a common feature in the Mediterranean forests through the years, as a wide tract of forest fortune is lost because of the incendiary fires in the forests. The enormous damages caused by forest fires enhanced the efforts of scientists towards the attenuation of the negative effects of forest fire and consequently the minimization of biodiversity losses by searching more for the adequate distribution of attempts on forest fire prevention and, suppression. The multi-temporal Principal Components Analysis is applied to a pair of images of consecutive years obtained from Landsat-8 satellite to unconventional map and assess the spatial extent of the burned areas on the island of Thasos, Greece. First, the PCA was applied on the before fire image, and then a multi-temporal image is created from the 3rd, 4th, and 5th band of before and after images including Normalized Difference Vegetation Index to enhance the results. The results from the different steps of this analysis robustly mapped the burned areas by 82.28 ha confirmed by almost 85%. Are compared with data provided by the local forest service in order to assess their accuracy. The multi-temporal PCA outputs including NDVI (PC 4, PC %, and PC 6) give better accuracy due to its ability to distinguish the burned areas of older years and to the Normalized Difference Vegetation Index that gives better variance to the image.

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