ESTIMATIVA DE MUDANÇAS TEMPORAIS COM O CÁLCULO DE ÍNDICES DE VEGETAÇÃO DO MUNICÍPIO DE ITABERÁ (SP)

2017 ◽  
Vol 32 (2) ◽  
pp. 195
Author(s):  
Ana Clara De Barros ◽  
Amanda Aparecida De Lima ◽  
Felipe De Souza Nogueira Tagliarini ◽  
Zacarias Xavier de Barros
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Near Infrared ◽  
Temporal Analysis ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Chlorophyll Contents ◽  
Spectral Bands ◽  
Vegetation Water ◽  
Images Processing

O presente trabalho teve como objetivo realizar a análise temporal da cobertura vegetal, num período de 10 anos do município de Itaberá-SP, utilizando os índices de vegetação NDVI e NDWI por meio de imagens de satélite. Do ano de 2005 foram utilizadas duas imagens do Landsat 5 de órbita/ponto 221/76 e 221/77 e uma imagem de 2015 do Landsat 8, órbita/ponto 221/76. As bandas espectrais utilizadas foram: 3,4 e 5 do Landsat 5 e 4,5 e 6 do Landsat 8 que correspondem aos comprimentos de ondas do vermelho (RED), infravermelho próximo (NIR) e infravermelho médio (SWIR1), respectivamente. Através das análises dos índices, constatou que as áreas que possuem baixos valores de NDVI também possuem baixos valores de NDWI, o que indica uma vegetação que sofre estresse hídrico e com baixo teor de clorofila. Os valores mais altos indicam vegetação fotossinteticamente ativa, que contêm maior teor de umidade.PALAVRAS-CHAVE: Sensoriamento remoto, processamento de imagens, cobertura vegetal. TEMPORAL ANALYSISUSING VEGETATION INDEX OF VEGETATION COVER IN ITABERA (SP)ABSTRACT: The objective of this work was to carry out the temporal analysis of the vegetation cover, in a period of 10 years of Itaberá-SP county, making use of the vegetation index NDVI and NDWI of satellites images. Two Landsat 5’s images of 2005 with path/row 221/76 and 221/77 and one Landsat 8’s image, path/row 221/76 were used. The spectral bands used ware: 3, 4 and 5 of the Landsat 5 and 4, 5 and 6 of the Landsat 8 that correspond to red waves lengths (RED), near infrared (NIR) and medium infrared (SWIR1), respectively. It was found that areas with low NDVI values also have low NDWI values, indicating vegetation water stress and low chlorophyll contents. The highest values indicate Photosynthetically active vegetation, which contain higher moisture contents.KEYWORDS: Remote sensing, images processing, vegetal cover.

scholarly journals Performance Evaluation of Newly Proposed Seaweed Enhancing Index (SEI)

2019 ◽  
Vol 11 (12) ◽  
pp. 1434 ◽  
Author(s):  
Muhammad Danish Siddiqui ◽  
Arjumand Z. Zaidi ◽  
Muhammad Abdullah
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Extensive Field ◽  
Reference Index ◽  
Almost All ◽  
Shortwave Infrared ◽  
Better Than

Seaweed is a valuable coastal resource for its use in food, cosmetics, and other items. This study proposed new remote sensing based seaweed enhancing index (SEI) using spectral bands of near-infrared (NIR) and shortwave-infrared (SWIR) of Landsat 8 satellite data. Nine Landsat 8 satellite images of years 2014, 2016, and 2018 for the January, February, and March months were utilized to test the performance of SEI. The seaweed patches in the coastal waters of Karachi, Pakistan were mapped using the SEI, normalized difference vegetation index (NDVI), and floating algae index (FAI). Seaweed locations recorded during a field survey on February 26, 2014, were used to determine threshold values for all three indices. The accuracy of SEI was compared with NDVI while placing FAI as the reference index. The accuracy of NDVI and SEI were assessed by matching their spatial extent of seaweed cover with FAI enhanced seaweed area. SEI images of January 2016, February 2018, and March 2018 enhanced less than 50 percent of the corresponding FAI total seaweed areas. However, on these dates the NDVI performed very well, matching more than 95 percent of FAI seaweed coverage. Except for these three times, the performance of SEI in the remaining six images was either similar to NDVI or even better than NDVI. SEI enhanced 99 percent of FAI seaweed cover on January 2018 image. Overall, seaweed area not covered by FAI was greater in SEI than NDVI in almost all images, which needs to be further explored in future studies by collecting extensive field information to validate SEI mapped additional area beyond the extent of FAI seaweed cover. Based on these results, in the majority of the satellite temporal images selected for this study, the performance of the newly proposed index—SEI, was found either better than or similar to NDVI.

scholarly journals Performance Assessment of Newly Developed Seaweed Enhancing Index

2018 ◽  
Author(s):  
Muhammad Danish Siddiqui ◽  
Arjumand Z. Zaidi ◽  
Muhammad Abdullah
Keyword(s):  
Coastal Waters ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Field Surveys ◽  
Spectral Bands ◽  
Breeding Grounds ◽  
Shortwave Infrared ◽  
Better Than

Seaweeds are regarded as one of the valuable coastal resources because of their usage in human food, cosmetics, and other industrial items. They also play a significant role in providing nourishment, shelter, and breeding grounds for fish and many other sea species. This study introduces a newly developed seaweed enhancing index (SEI) using spectral bands of near-infrared (NIR) and shortwave infrared (SWIR) of Landsat 8 satellite data. The seaweed patches in the coastal waters of Karachi, Pakistan were mapped using SEI, and its performance was compared with other commonly used indices - Normalized Difference Vegetation Index (NDVI) and Floating Algae Index (FAI). The accuracy of the mapping results obtained from SEI, NDVI, and FAI was checked with field verified seaweed locations. The purpose of the field surveys was to validate the results of this study and to evaluate the performance of SEI with NDVI and FAI. The performance of SEI was found better than NDVI and FAI in enhancing submerged patches of the seaweed pixels what other indices failed to do.

scholarly journals Monitoring infestations of oak forests by Tortrix viridana (Lepidoptera: Tortricidae) using remote sensing

2016 ◽  
Vol 52 (No. 4) ◽  
pp. 270-276 ◽  
Author(s):  
Gooshbor Leila ◽  
Bavaghar Mahtab Pir ◽  
Amanollahi Jamil ◽  
Ghobari Hamed
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Insect Herbivory ◽  
Oak Forests ◽  
Landsat 8 ◽  
Landsat Images ◽  
Leaf Roller ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Tortrix Viridana

We tested the suitability of Landsat images to track defoliation by insect herbivory with focus on the oak leaf roller, Tortrix viridana (Lep.: Tortricidae). Landsat images from the period before (2002) and after the T. viridana infestation (2007, 2014) were compared in oak forests of Zagros in western Iran. The Normalised Difference Vegetation Index (NDVI) was calculated for the test area from Landsat 5, 7, and 8 images. Because the red and near-infrared spectral bands of Landsat 8 OLI sensors are different from the other two, a model for the calibration of Landsat OLI NDVI was developed. The proposed model with a correlation coefficient of 0.928 and root mean square error of 0.05 turned out to be applicable and the NDVI decreased significantly during the observation period. Taking into account the protection status of the area and small fluctuations in temperature, the decrease in NDVI could be attributed to T. viridana damage.

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.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

scholarly journals Remote Sensing Landslide Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

scholarly journals Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

2018 ◽  
Vol 10 (8) ◽  
pp. 1248 ◽  
Author(s):  
Hua Sun ◽  
Qing Wang ◽  
Guangxing Wang ◽  
Hui Lin ◽  
Peng Luo ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Good Alternative ◽  
Landsat 8 ◽  
Arid Areas ◽  
Landsat Images ◽  
Infrared Band ◽  
K Value ◽  
Mean Square Errors ◽  
Semi Arid

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons-kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.

scholarly journals Geospatial Monitoring on Land Surface Temperature and Vegetation Dynamics: A Case of a City Area in Khulna, Bangladesh

2020 ◽  
Vol 3 (2) ◽  
pp. a35-43
Author(s):  
MD. NAZMUL HAQUE ◽  
NOWRIN RAHMAN KHANAM ◽  
MEHNAZ NANJIBA
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Environmental Condition ◽  
Infrared Sensor ◽  
The Third ◽  
Canopy Area

Land surface temperature and vegetation cover are two important parameters to evaluate the climate change and environmental condition. The current study is carried out in respect of monitoring the changing phenomena of climate and environment. The area selected to conduct the study was ward number 1, 2 and 3 of Khulna City Corporation), from the third largest city of Bangladesh. This study is corresponding through the calculation of Land Surface Temperature (LST) and Normalized Differential Vegetation Index (NDVI) for two different years, 2010 and 2018. LST and NDVI are observed to realize the association between surface temperature and amount of vegetation. With the help of ArcGIS 10.5, LST and NDVI calculations are done using Landsat 5 Thermal Mapper, Landsat 8 Operational Land Imager and Thermal Infrared Sensor images (for 2010 and 2018, respectively) collected from USGS Earth Explorer. The findings of the study specify that the highest temperature in 2018 is 32.5˚C in ward 2 and in 2010 it was 27.5˚C in ward 3, though the overall vegetation amount decreased in 2018, About 18, 900 square meter of very low canopy area has increased in ward 3 from the period of 2010 to 2018 and in the same time 35, 100 square meter of low canopy area has been decreased for the overall study area. However, parts of the study area of ward no. 3 had faced a significant increase in vegetation cover which is the cause of low temperature compared to ward 1 and 2 in 2018.

scholarly journals A Long-term Spatial and Temporal Analysis of NDVI Changes in Java Island Using Google Earth Engine

2021 ◽  
Vol 936 (1) ◽  
pp. 012038
Author(s):  
Benedict ◽  
Lalu Muhamad Jaelani
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Data ◽  
Temporal Analysis ◽  
Google Earth ◽  
Vegetation Density ◽  
Angle Sensor ◽  
Study Results ◽  
Google Earth Engine

Abstract Java is Indonesia’s and the world’s most populous island. The increase in population on the island of Java reduces the area of forest and other vegetation covers. Landslides, floods, and other natural disasters are caused by reduced vegetation cover. Furthermore, it has the potential to lead to the extinction of flora and fauna. The Normalized Difference Vegetation Index (NDVI) can be used to monitor the vegetation cover. This study analyzes the NDVI changes value from 2005 to 2020 using Terra and Aqua MODIS image data processed using Google Earth Engine. Processing was carried out in some stages: down-setting, performing NDVI processing, calculating monthly average NDVI, calculating annual average NDVI, and analyzing. From the study results, the NDVI value of Terra and Aqua MODIS data has a solid but imperfect correlation coefficient due to differences in orbital time which causes differences in solar zenith angle, sensor viewing angle, and azimuth angle. Then from this study, it was found that overall, changes in vegetation density cover on the island of Java decreased, which was indicated by the NDVI decline rate of -0.00047/year. The most significant decrease in NDVI value occurred in the period 2015–2016, covering an area of 13994.630 km2, and the most significant increase in NDVI occurred in the period 2010–2011, covering an area of 2256.101 km2.

scholarly journals SATELLITE-DRIVEN ASSESSMENT OF SURFACE URBAN HEAT ISLANDS IN THE CITY OF ZAGREB, CROATIA

2020 ◽  
Vol V-3-2020 ◽  
pp. 757-764
Author(s):  
A. Krtalić ◽  
A. Kuveždić Divjak ◽  
K. Čmrlec
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Principal Component ◽  
Urban Heat Islands ◽  
City Centre ◽  
Landsat 8 ◽  
Heat Islands ◽  
Critical Areas ◽  
Urban Heat ◽  
The City

Abstract. This study aims to assess surface urban heat islands (SUHIs) pattern over the city of Zagreb, Croatia, based on satellite (optical and thermal) remote sensing data. The spatio-temporal identification of SUHIs is analysed using the 12 sets of Landsat 8 imagery acquired during 2017 (in each month of the year). Vegetation cover within the city boundaries is extracted by using Principal Component Analysis (PCA) data fusion method on calculated three vegetation indices (VI): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Ratio Vegetation Index (RVI) for each set of bands. The first principal component was used to compute the land surface temperature (LST) and deductive Environmental Criticality Index (ECI). As expected, the relationship between LST and all VI scores shows a negative correlation and is most negative with RVI. The environmentally critical areas and the patterns of seasonal variations of the SUHIs in the city of Zagreb were identified based on the LST, ECI and vegetation cover. The city centre, an industrial area in the eastern part and an area with shopping centers and commercial buildings in the western part of the city were identified as the most critical areas.

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