scholarly journals Using Remote Sensing for Identifying Suitable Areas for Flood Shelter: A Case Study of Thatta, Sindh Pakistan

2020 ◽  
Vol 11 (1) ◽  
pp. 95-101
Author(s):  
Umer Saleem ◽  
Takeshi Mizunoya ◽  
Yabar Helmut ◽  
Ammara Ajmal
Keyword(s):  
Remote Sensing ◽  
Natural Disasters ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Flood Hazard ◽  
Flood Management ◽  
The World ◽  
Development Plans ◽  
Flooding Events

The most recurring type of disaster in the world these days is flood because of the spread and extent of its effect on people, among all-natural disasters of the world. Human activities have paved the way for many of these flood behavior to change as they used to be in the past. Pakistan experienced one of the most devastating natural disasters in its history all across the country in 2010, but Thatta district in southern part got severely affected during this flood. For the research, a simple yet efficient methodology Normalized Difference Vegetation Index (NDVI) by using remote sensing images for identifying flood hazard areas was utilized. Geographic Information Systems (GIS) helps in finding shelter areas with a minimum effect of floods. It is essential to realize the importance of mapped results in consideration of manual flood management in future. The method used in this study is robust enough to explain the flood hazard for suggesting suitable shelter sites in case of flooding events. This would help disaster management bodies and other related agencies to formulate the development plans while keeping the hazard areas, which are unsuitable for development due to flood risk in the future.

scholarly journals Using Remote Sensing for Identifying Suitable Areas for Flood Shelter: A Case Study of Thatta, Sindh Pakistan

2020 ◽  
Vol 11 (1) ◽  
pp. 95-101
Author(s):  
Umer Saleem ◽  
Takeshi Mizunoya ◽  
Yabar Helmut ◽  
Ammara Ajmal
Keyword(s):  
Remote Sensing ◽  
Natural Disasters ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Flood Hazard ◽  
Flood Management ◽  
The World ◽  
Development Plans ◽  
Flooding Events

The most recurring type of disaster in the world these days is flood because of the spread and extent of its effect on people, among all-natural disasters of the world. Human activities have paved the way for many of these flood behavior to change as they used to be in the past. Pakistan experienced one of the most devastating natural disasters in its history all across the country in 2010, but Thatta district in southern part got severely affected during this flood. For the research, a simple yet efficient methodology Normalized Difference Vegetation Index (NDVI) by using remote sensing images for identifying flood hazard areas was utilized. Geographic Information Systems (GIS) helps in finding shelter areas with a minimum effect of floods. It is essential to realize the importance of mapped results in consideration of manual flood management in future. The method used in this study is robust enough to explain the flood hazard for suggesting suitable shelter sites in case of flooding events. This would help disaster management bodies and other related agencies to formulate the development plans while keeping the hazard areas, which are unsuitable for development due to flood risk in the future.

scholarly journals Applications of Satellite Remote Sensing for Archaeological Survey: A Case Study from the Sinis Archaeological Project, Sardinia

2020 ◽  
Vol 8 (2) ◽  
pp. 192-205
Author(s):  
Daniel Plekhov ◽  
Linda R. Gosner ◽  
Alexander J. Smith ◽  
Jessica Nowlin
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Daily Basis ◽  
Archaeological Survey ◽  
Short Term ◽  
Spectral Bands ◽  
Archaeological Project

ABSTRACTSatellite imagery has long been recognized as well suited for the regional and ecological questions of many archaeological surveys. One underexplored aspect of such data is their temporal resolution. It is now possible for areas to be imaged on an almost daily basis, and this resolution offers new opportunities for studying landscapes through remote sensing in parallel with ground-based survey. This article explores the applications of these data for visibility assessment and land-cover change detection in the context of the Sinis Archaeological Project, a regional archaeological survey of west-central Sardinia. We employ imagery provided by Planet, which has a spatial resolution of 3 m, in four spectral bands, and is collected daily. Using Normalized Difference Vegetation Index (NDVI) values calculated for each survey unit, we find that there is a relationship between NDVI values and field-reported visibility in general, though the strength of this correlation differs according to land-cover classes. We also find the data to be effective at tracking short-term changes in field conditions that allow us to differentiate fields of similar land cover and visibility. We consider limitations and potentials of these data and encourage further experimentation and development.

scholarly journals Geostatistical Analysis of the Spatial Correlation between Territorial Anthropization and Flooding Vulnerability: Application to the DANA Phenomenon in a Mediterranean Watershed

2021 ◽  
Vol 11 (2) ◽  
pp. 809
Author(s):  
Salvador Garcia-Ayllon ◽  
John Radke
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geostatistical Analysis ◽  
Index Analysis ◽  
Flooding Events ◽  
The Impact ◽  
Mediterranean Coastal Lagoon ◽  
Hydrographic Network

Climate change is making intense DANA (depresión aislada en niveles altos) type rains a more frequent phenomenon in Mediterranean basins. This trend, combined with the transformation of the territory derived from diffuse anthropization processes, has created an explosive cocktail for many coastal towns due to flooding events. To evaluate this problem and the impact of its main guiding parameters, a geostatistical analysis of the territory based on GIS indicators and an NDVI (Normalized Difference Vegetation Index) analysis is developed. The assessment of the validity of a proposed methodology is applied to the case study of the Campo de Cartagena watershed located around the Mar Menor, a Mediterranean coastal lagoon in Southeastern Spain. This area has suffered three catastrophic floods derived from the DANA phenomenon between 2016 and 2019. The results show that apart from the effects derived from climate change, the real issue that amplifies the damage caused by floods is the diffuse anthropization process in the area, which has caused the loss of the natural hydrographic network that traditionally existed in the basin.

scholarly journals MAPPING AND MONITORING WETLANDS USING SENTINEL-2 SATELLITE IMAGERY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 271-277 ◽  
Author(s):  
G. Kaplan ◽  
U. Avdan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Kappa Coefficient ◽  
Water Index ◽  
Object Based ◽  
The World ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Mapping and monitoring of wetlands as one of the world`s most valuable natural resource has gained importance with the developed of the remote sensing techniques. This paper presents the capabilities of Sentinel-2 successfully launched in June 2015 for mapping and monitoring wetlands. For this purpose, three different approaches were used, pixel-based, object-based and index-based classification. Additional, for more successful extraction of wetlands, a combination of object-based and index-based method was proposed. It was proposed the use of object-based classification for extraction of the wetlands boundaries and the use of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for classifying the contents within the wetlands boundaries. As a study area in this paper Sakarbasi spring in Eskisehir, Turkey was chosen. The results showed successful mapping and monitoring of wetlands with kappa coefficient of 0.95.

scholarly journals Applications of Satellite Remote Sensing for Archaeological Survey: A Case Study from the Sinis Archaeological Project, Sardinia

2020 ◽  
Author(s):  
Daniel Plekhov ◽  
Linda R. Gosner ◽  
Alexander J. Smith ◽  
Jessica Nowlin
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Daily Basis ◽  
Archaeological Survey ◽  
Short Term ◽  
Spectral Bands ◽  
Archaeological Project

Satellite imagery has long been recognized as well suited for the regional and ecological questions of many archaeological surveys. One underexplored aspect of such data is their temporal resolution. It is now possible for areas to be imaged on an almost daily basis, and this resolution offers new opportunities for studying landscapes through remote sensing in parallel with ground-based survey. This article explores the applications of these data for visibility assessment and land-cover change detection in the context of the Sinis Archaeological Project, a regional archaeological survey of west-central Sardinia. We employ imagery provided by Planet, which has a spatial resolution of 3 m, in four spectral bands, and is collected daily. Using Normalized Difference Vegetation Index (NDVI) values calculated for each survey unit, we find that there is a relationship between NDVI values and field-reported visibility in general, though the strength of this correlation differs according to land-cover classes. We also find the data to be effective at tracking short-term changes in field conditions that allow us to differentiate fields of similar land cover and visibility. We consider limitations and potentials of these data and encourage further experimentation and development.

scholarly journals Ranking of Illegal Buildings Close to Rivers: A Proposal, Its Implementation and Preliminary Validation

2019 ◽  
Vol 8 (11) ◽  
pp. 510 ◽  
Author(s):  
Paolino Di Felice
Keyword(s):  
Remote Sensing ◽  
Input Data ◽  
Flood Hazard ◽  
Second Step ◽  
Preliminary Validation ◽  
Specific Category ◽  
The World ◽  
Remote Sensing Methods ◽  
Unsolved Issue

Illegal buildings (IBs) are a dramatic problem in developing countries due to the population explosion, but, at the same time, they represent an unsolved issue in several states usually called advanced (as, for instance, Italy). To protect the environment, and hence, people, land authorities must respond to the challenge of IBs by demolishing them. However, in countries where the phenomenon is extended, it is indispensable to provide those figures with an IT tool that returns to them an order of demolition. Through remote sensing methods, suspicious buildings can be identified with a good approximation, but they are all ex aequo. The research summarized in this paper formalizes a two-steps method to deal with a specific category of IBs, namely, those that are close to rivers. These buildings are of special interest to land authorities because people living or simply working inside them are exposed to the flood hazard that each year claims many victims all over the world. The first step of the method computes the census of the IBs located close to rivers, while the second step computes the ranking of these buildings. The ranking may be used as the IBs demolition order. In the paper, it is also proposed the structure of a Spatial DataBase (briefly, SDB) that is suitable to store the input data necessary to solve the problem, as well as the final ranking. Spatial SQL queries against the SDB implement the proposed two-steps method. A real case study was carried out to make a preliminary validation of the method.

scholarly journals Assessing the Effects of Photovoltaic Powerplants on Surface Temperature Using Remote Sensing Techniques

2020 ◽  
Vol 12 (11) ◽  
pp. 1825
Author(s):  
Xunhe Zhang ◽  
Ming Xu
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rapid Development ◽  
Cooling Effect ◽  
Surface Cooling ◽  
Thermal Infrared Remote Sensing ◽  
The World

The rapid development of photovoltaic (PV) powerplants in the world has drawn attention on their climate and environmental impacts. In this study, we assessed the effects of PV powerplants on surface temperature using 23 largest PV powerplants in the world with thermal infrared remote sensing technique. Our result showed that the installation of the PV powerplants had significantly reduced the daily mean surface temperature by 0.53 °C in the PV powerplant areas. The cooling effect with the installation of the PV powerplants was much stronger during the daytime than the nighttime with the surface temperature dropped by 0.81 °C and 0.24 °C respectively. This cooling effect was also depended on the capacity of the powerplants with a cooling rate of −0.32, −0.48, and −0.14 °C/TWh, respectively, for daily mean, daytime, and nighttime temperature. We also found that the construction of the powerplants significantly decreased the surface albedo from 0.22 to 0.184, but significantly increased the effective albedo (surface albedo plus electricity conversion) from 0.22 to 0.244, suggesting conversion of solar energy to electrical energy is a major contributor to the observed surface cooling. Our further analyses showed that the nighttime cooling in the powerplants was significantly correlated with the latitude and elevation of the powerplants as well as the annual mean temperature, precipitation, solar radiation, and normalized difference vegetation index (NDVI). This means the temperature effect of the PV powerplants depended on regional geography, climate and vegetation conditions. This finding can be used to guide the selection of the sites of PV powerplants in the future.

TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

2019 ◽  
Vol 21 (2) ◽  
pp. 1310-1320
Author(s):  
Cícera Celiane Januário da Silva ◽  
Vinicius Ferreira Luna ◽  
Joyce Ferreira Gomes ◽  
Juliana Maria Oliveira Silva
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Temperature Measurement ◽  
Rainy Season ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Low Density ◽  
Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia.                                                                                  ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

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