scholarly journals Estimation of the Temporal Change in Carbon Stock of Muthupet Mangroves in Tamil Nadu Using Remote Sensing Techniques

2019 ◽  
pp. 1-7
Author(s):  
K. Narmada ◽  
K. Annaidasan
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Tamil Nadu ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Reliability And Validity ◽  
Avicennia Marina ◽  
Policy Makers ◽  
Remote Sensing Techniques ◽  
The Impact

Aim: To study the carbon storage potential of Muthupet mangroves in Tamil Nadu using Remote sensing techniques. Place and Duration: The study is carried out in Muthupet Mangroves for the years 2000, 2010 and 2017. Methodology: In this study the remote sensing images were processed using the ERDAS and ArcGIS software and the NDVI (Normalized Difference Vegetation Index) has also been applied to estimate the quantity of carbon sequestration capability for the Avicennia marina mangrove growing in the Muthupet region for the period 2000-2017. The formula proposed by Lai [10] was used to calculate the carbon stock using geospatial techniques. Results: The results show that the mangroves in Muthupet region has NDVI values between -0.671 and 0.398 in 2000, -0.93 and 0.621 in 2010 and -0.66 and 0.398 in 2017. The observation indicates the reliability and validity of the aviation remote sensing with high resolution and with near red spectrum experimented in this research for estimating the the Avicennia marina (Forsk.) mangrove growing in this region. The estimated quantity of carbon di oxide sequestrated by the mangrove was about 1475.642 Mg/Ha in 2000, 3646.312 Mg/Ha in 2010 and 1677.72 Mg/Ha in 2017. Conclusion: The capacity of the Avicennia marina growing in Muthupet region to sequestrate carbon show that it has a great potential for development and implementation. The results obtained in this research can be used as a basis for policy makers, conservationists, regional planners, and researchers to deal with future development of cities and their surroundings in regions of highly ecological and environmental sensitivity. Thus the finding shows that wetlands are an important ecological boon as it helps to control the impact of climate change in many different ways.

scholarly journals Monitoring the Recent Activity of Landslides in the Mailuu-Suu Valley (Kyrgyzstan) Using Radar and Optical Remote Sensing Techniques

Geosciences ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 164
Author(s):  
Valentine Piroton ◽  
Romy Schlögel ◽  
Christian Barbier ◽  
Hans-Balder Havenith
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mining Area ◽  
Mass Movements ◽  
Optical Remote Sensing ◽  
Velocity Anomaly ◽  
In Situ Data ◽  
Remote Sensing Techniques ◽  
The Impact

Central Asian mountain regions are prone to multiple types of natural hazards, often causing damage due to the impact of mass movements. In spring 2017, Kyrgyzstan suffered significant losses from a massive landslide activation event, during which also two of the largest deep-seated mass movements of the former mining area of Mailuu-Suu—the Koytash and Tektonik landslides—were reactivated. This study consists of the use of optical and radar satellite data to highlight deformation zones and identify displacements prior to the collapse of Koytash and to the more superficial deformation on Tektonik. Especially for the first one, the comparison of Digital Elevation Models of 2011 and 2017 (respectively, satellite and unmanned aerial vehicle (UAV) imagery-based) highlights areas of depletion and accumulation, in the scarp and near the toe, respectively. The Differential Synthetic Aperture Radar Interferometry analysis identified slow displacements during the months preceding the reactivation in April 2017, indicating the long-term sliding activity of Koytash and Tektonik. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of both landslides. Furthermore, the analysis of the Normalized Difference Vegetation Index revealed land cover changes associated with the sliding process between June 2016 and October 2017. In addition, in situ data from a local meteorological station highlighted the important contribution of precipitation as a trigger of the collapse. The multidirectional approach used in this study demonstrated the efficiency of applying multiple remote sensing techniques, combined with a meteorological analysis, to identify triggering factors and monitor the activity of landslides.

scholarly journals Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

2019 ◽  
Vol 26 (3) ◽  
pp. 117
Author(s):  
Tri Muji Susantoro ◽  
Ketut Wikantika ◽  
Agung Budi Harto ◽  
Deni Suwardi
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Landsat 8 ◽  
Gas Field ◽  
Research Results ◽  
The Third ◽  
Third Phase ◽  
The Impact

This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.

scholarly journals MONITORING DESERTIFICATION IN SOME REGIONS OF IRAQ USING GIS TECHNIQUES

2021 ◽  
Vol 52 (3) ◽  
pp. 620-625
Author(s):  
Y. K. Al-Timimi
Keyword(s):  
Remote Sensing ◽  
Environmental Economic ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Marked Decrease ◽  
Social Systems ◽  
Sand Dunes ◽  
Rate Of Increase ◽  
Remote Sensing Techniques

Desertification is one of the phenomena that threatening the environmental, economic, and social systems. This study aims to evaluate and monitor desertification in the central parts of Iraq between the Tigris and Euphrates rivers through the use of remote sensing techniques and geographic information systems. The Normalized difference vegetation index NDVI and the crust index CI were used, which were applied to two of the Landsat ETM + and OLI satellite imagery during the years 1990 and 2019. The research results showed that the total area of ​​the vegetation cover was 2620 km2 in 1990, while there was a marked decrease in the area Vegetation cover 764 km2 in 2019, accounting for 34.8% (medium desertification) and 10.2% (high desertification), respectively. Also, the results showed that sand dunes occupied an area of ​​767 km2 in 1990, while the area of ​​sand dunes increased to 1723 km2 in 2019, with a rate of 10.2%) medium desertification (and 22.9% (severe desertification), respectively. It was noted that the overall rate of decrease in vegetation cover was 21.33 km2year-1 while the overall rate of increase in ground erosion in the area is 10.99 km2year-1.

scholarly journals Analyzing the Relationship between Animal Diversity and the Remote Sensing Vegetation Parameters: The Case of Xinjiang, China

2021 ◽  
Vol 13 (17) ◽  
pp. 9897
Author(s):  
Jinhui Wu ◽  
Haoxin Li ◽  
Huawei Wan ◽  
Yongcai Wang ◽  
Chenxi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Bird Diversity ◽  
Area Index ◽  
Sensing Data ◽  
Animal Diversity ◽  
The Mean ◽  
The Impact

An explicit analysis of the impact for the richness of species of the vegetation phenological characteristics calculated from various remote sensing data is critical and essential for biodiversity conversion and restoration. This study collected long-term the Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and the Fractional Vegetation Cover (FVC), and calculated the six vegetation phenological characteristic parameters: the mean of the growing season, the mean of the mature season, the mean of the withered season, the annual difference value, the annual cumulative value, and the annual standard deviation in the Xinjiang Uygur Autonomous Region. The relationships between the vegetation phenological characteristics and the species richness of birds and mammals were analyzed in spatial distribution. The main findings include: (1) The correlation between bird diversity and vegetation factors is greater than that of mammals. (2) For remote sensing data, FAPAR is the most important vegetation parameter for both birds and mammals. (3) For vegetation phenological characteristics, the annual cumulative value of the LAI is the most crucial vegetation phenological parameter for influencing bird diversity distribution, and the annual difference value of the NDVI is the most significant driving factor for mammal diversity distribution.

scholarly journals Application of Remote Sensing on El Niño Extreme Effect in Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)

2021 ◽  
Vol 6 (1) ◽  
pp. 46-56
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Remote Sensing ◽  
El Niño ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
El Nino ◽  
Extreme Temperature ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
The Impact

The years 1997/1998 and 2015/2016 saw the worst El Niño occurrence in human history. The occurrence of El Niño causes extreme temperature events which are higher than usual, drought and prolonged drought. The incident caused a decline in the ability of plants in carrying out the process of photosynthesis. This causes the carbon dioxide content to be higher than normal. Studies on the effects of El Niño and its degree of strength are still under-studied especially by researchers in the tropics. This study uses remote sensing technology that can provide spatial information. The first step of remote sensing data needs to go through the pre-process before building the NDVI (Normalized Difference Vegetation Index) and Normalized Difference Water Index (NDWI) maps. Next this study will identify the relationship between Oceanic Nino Index (ONI) with Application Remote Sensing in The Study Of El Niño Extreme Effect 1997/1998 and 2015/2016 On Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)NDWI and NDWI landscape indices. Next will make a comparison, statistical and spatial information space between NDWI and NDVI for each year 1997/1998 and 2015/2016. This study is very important in providing spatial information to those responsible in preparing measures in reducing the impact of El Niño.

scholarly journals EMPREGO DO SENSORIAMENTO REMOTO PARA ANÁLISE DO USO E OCUPAÇÃO DO SOLO NO PERÍMETRO IRRIGADO VÁRZEAS DE SOUSA-PB

Irriga ◽  
2022 ◽  
Vol 1 (4) ◽  
pp. 722-729
Author(s):  
LEONCIO GONÇALVES RODRIGUES ◽  
ANA CÉLIA MAIA MEIRELES ◽  
CARLOS WAGNER OLIVEIRA
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Sustainable Development ◽  
Water Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ideal Tool ◽  
Major Producer ◽  
Irrigated Perimeter ◽  
Remote Sensing Techniques

EMPREGO DO SENSORIAMENTO REMOTO PARA ANÁLISE DO USO E OCUPAÇÃO DO SOLO NO PERÍMETRO IRRIGADO VÁRZEAS DE SOUSA-PB     LEONCIO GONÇALVES RODRIGUES1; ANA CÉLIA MAIA MEIRELES2 E CARLOS WAGNER OLIVEIRA3   1Mestrando em Desenvolvimento Regional Sustentável, Universidade Federal do Cariri-UFCA, Rua Ícaro Moreira de Sousa, nº 126, Muriti, 63130-025, Crato, Ceará, Brasil, [email protected]. 2 Professora titular do Programa de pós graduação em Desenvolvimento Regional Sustentável, Universidade Federal do Cariri-UFCA, Rua Ícaro Moreira de Sousa, nº 126, Muriti, 63130-025, Crato, Ceará, Brasil, [email protected]  3 Professor titular do Programa de pós graduação em Desenvolvimento Regional Sustentável, Universidade Federal do Cariri-UFCA, Rua Ícaro Moreira de Sousa, nº 126, Muriti, 63130-025, Crato, Ceará, Brasil, [email protected]     1 RESUMO   O perímetro irrigado várzeas de Sousa (PIVAS) é um grande produtor de culturas como coco, banana, sorgo, algodão dentre outras. Tem grande importância para o desenvolvimento econômico da região do alto sertão da Paraíba. Possui características impares como a distribuição de água para todos os lotes por potencial gravitacional. Para a sustentabilidade do perímetro é necessário o monitoramento constante de suas áreas, para se poder desenvolver estratégias que auxiliam no desenvolvimento sustentável. Nesse sentido, o sensoriamento remoto é uma ferramenta ideal por permitir a obtenção rápida e precisa de informações sobre uma área, o que pode auxiliar na tomada de decisão. Partindo desse pressuposto, o objetivo deste trabalho é apresentar um conjunto de técnicas de sensoriamento que possibilitem o monitoramento de áreas irrigadas ou ambientais. Para tanto foi determinado do uso e ocupação do solo, o índice de vegetação por diferença normalizada (NDVI) e o índice de vegetação ajustado ao solo (SAVI) para o PIVAS. Onde se observou que as técnicas de sensoriamento remoto auxiliam na compreensão de áreas no espaço e tempo.   Palavras-chave: monitoramento, manejo, satélite.     RODRIGUES, L. G.; MEIRELES, A. C. M.; OLIVEIRA, C, W. USE OF REMOTE SENSING TO ANALYZE THE USE AND OCCUPANCY OF THE SOIL IN THE PERIMETER IRRIGATED VÁRZEAS DE SOUSA-PB.     2 ABSTRACT   The floodplain-irrigated perimeter of Sousa (PIVAS) is a major producer of crops such as coconut, banana, sorghum, cotton, among others. It is of great importance for the economic development of the upper wilderness region of Paraiba. It has unique characteristics such as water distribution to all lots by gravitational potential. For the sustainability of the perimeter, constant monitoring of its areas is necessary, to be able to develop strategies that help in sustainable development. In this sense, remote sensing is an ideal tool as it allows for quick and accurate obtaining information about an area, which can help in decision making. Based on this assumption, this work aims to present a set of sensing techniques that enable monitoring of irrigated or environmental areas. For this purpose, the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI) were determined for the PIVAS. Where it was observed that remote sensing techniques help understand areas in space and time.   Keywords: monitoring, management, satellite.

scholarly journals Classification of a burnt area based on spectral images

2018 ◽  
Vol 247 ◽  
pp. 00017
Author(s):  
Anna Szajewska
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Burnt Area ◽  
Forest Protection ◽  
The Earth ◽  
Remote Sensing Techniques ◽  
Spectral Ranges ◽  
Mathematical Operations

The use of remote sensing techniques allows obtaining information about processes that occur on the surface of the Earth. In the aspects of fire protection and forest protection, it is important to know a burnt area which was created as a result of a fire of the soil cover or a total fire. The knowledge of this area is necessary to assess losses. Remote sensing techniques allow obtaining images in various spectral ranges. Remote sensing satellites offer multi-band data. Mathematical operations that operate on values coming from different spectral ranges allow determining various remote sensing indicators. The manuscript presents the possibility of using the NDVI (Normalized Difference Vegetation Index) to classify the burnt area. The NDVI is relatively easy to obtain because it operates in the spectral ranges from 630 up to 915 nm, and is obtainable with one detector only. Thus, it can be obtained without any major problems using unmanned aerial vehicles, regardless of time and cloudiness, as is the case when acquiring satellite images. The manuscript describes experimental research and presents the results.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

scholarly journals Soil erosion and sediment transport modelling using hydrological models and remote sensing techniques in Wadi Billi, Egypt

2021 ◽  
Author(s):  
O. Almasalmeh ◽  
Ahmed Adel Saleh ◽  
Khaldoon A. Mourad
Keyword(s):  
Sediment Transport ◽  
Soil Erosion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Transport Modelling ◽  
Upland Erosion ◽  
Sediment Transport Modelling ◽  
Remote Sensing Techniques ◽  
The Impact

AbstractModelling soil erosion and sediment transport are vital to assess the impact of the flash floods. However, limited research works have studied sediment transport, especially in Egypt. This paper employs the HEC-HMS lumped hydrological model to predict the sediment load due to the flood event of 9th March 2014 in Wadi Billi, Egypt. The Modified USLE model has been used to calculate the total upland erosion, while Laursen-Copeland has been used to simulate load streams’ sediment transport potential. The Normalized Difference Vegetation Index (NDVI) has been applied over Landsat 8 image captured on 20th February 2014 using ArcMap 10.5 to determine the vegetation cover based on its spectral footprint. The resulted sedigraph showed accumulation of more than five thousand tons of sediments at the Wadi’s outlet. The results are crucial to design a suitable stormwater management system to protect the downstream urban area and to use flood water for groundwater recharge.

scholarly journals COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

2018 ◽  
Vol XLII-3 ◽  
pp. 1381-1387 ◽  
Author(s):  
M. Piragnolo ◽  
G. Lusiani ◽  
F. Pirotti
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Monitoring And Control ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.

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