Remote Sensing Technique
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2022 ◽  
Vol 46 (1) ◽  
pp. 75-80
R. C. DUBEY ◽  

The spectral radiance characteristics and vegetation indices like simple difference, ratio vegetation, normalised vegetation perpendicular vegetation transformed vegetation and tasseled cap transformation of mung been sunflower and groundnut crops at different growth stages have been studied. The experiment was conducted in post rainy season during 1990-91 in the farm of Agricultural College. Pune using hand held multi-spectral radiometer. The significance of spectral variation of radiance and vegetative indices with respect to the phenological stages are discussed.   

2022 ◽  
Vol 14 (1) ◽  
pp. 189
Tae-Min Oh ◽  
Seungil Baek ◽  
Tae-Hyun Kong ◽  
Sooyoon Koh ◽  
Jaehun Ahn ◽  

Titanium dioxide (TiO2) is a photocatalyst that can be used to remove nitrogen oxide (NOx). When applied to cementitious materials, it reacts with photons in sunlight or artificially generated light to reduce the concentration of particulate matter in the atmosphere. The concentration of TiO2 applied to the cementitious surface is difficult to quantify in a non-destructive manner after its application; however, knowledge of this residual amount is important for inspection and the evaluation of life expectancy. This study proposes a remote sensing technique that can estimate the concentration of TiO2 in the cementitious surface using a hyperspectral sensor. In the experiment, cement cores of varying TiO2 concentration and carbon contents were prepared and the surfaces were observed by TriOS RAMSES, a directional hyperspectral sensor. Machine-learning-based algorithms were then trained to estimate the TiO2 concentration under varying base material conditions. The results revealed that the best-performing algorithms produced TiO2 concentration estimates with a ~6% RMSE and a correlation close to 0.8. This study presents a robust machine learning model to estimate TiO2 and activated carbon concentration with high accuracy, which can be applied to abrasion monitoring of TiO2 and activated carbon in concrete structures.

2021 ◽  
Vol 14 (6) ◽  
pp. 3592
Haylla Rebeka De Albuquerque Lins Leonardo ◽  
Camila Oliveira de Britto Salgueiro ◽  
Débora Natália Oliveira de Almeida ◽  
Sylvana Melo dos Santos ◽  
Leidjane Maria Maciel de Oliveira

O Sertão Pernambucano é caracterizado por longos períodos de secas, com um regime pluviométrico inconstante e irregular, dificultando o desenvolvimento socioeconômico da região. Neste contexto a aplicação de técnica de Sensoriamento Remoto utilizando de imagens georreferenciadas destaca-se pela relevância no monitoramento e análise da variação da cobertura vegetal e do suprimento hídrico nos reservatórios da região. Este estudo objetivou-se em avaliar as variações temporais geoespacializadas do uso e ocupação do solo, vegetação e área superficial do espelho d’água do reservatório de Poço da Cruz - PE, em uma perspectiva espectro temporal utilizando imagens datadas de 2000, 2013 e 2020, aplicando os índices espectrais MNDWI, NDWI, SAVI, IAF, dos sistemas sensores TM Landsat 5 e OLI Landsat 8, e ferramentas do projeto MAPBIOMAS da coleção 5.0. A análise do MNDWI identificou o aumento na área superficial do reservatório ao longo dos anos, ressaltando que os anos de 2000 e 2013 apresentaram um maior estresse hídrico com redução dos valores do índice. Os índices NDWI, SAVI e IAF, apontaram uma cobertura vegetal escassa e seca com baixa umidade para os anos de 2000 e 2013, entretanto, observou-se o aumento do vigor vegetativo e presença de maior umidade para o ano de 2020. Condizente com os dados obtidos para o uso e ocupação do solo pelo projeto MAPBIOMAS, indicando que houve um aumento das áreas destinadas a agricultura e pastagem no entorno do reservatório entre os anos de 2000 e 2013, bem como o incremento do seu espelho d´água.   Analysis of the Temporal Variability of Water Body in the Backwoods of the Pernambuco A B S T R A C TThe Sertão Pernambucano is characterized by long periods of drought, with an unstable and irregular rainfall regime, which hinders the socioeconomic development of the region. In this context, the application of the Remote Sensing technique using georeferenced images stands out for its relevance in monitoring and analyzing the variation in vegetation cover and water supply in the region's reservoirs. This study aimed to evaluate the geospatial temporal variations of the use and occupation of the soil, vegetation and surface area of the water mirror of the Poço da Cruz reservoir - PE, in a temporal spectrum perspective using images dated from 2000, 2013 and 2020, applying the spectral indices MNDWI, NDWI, SAVI, IAF, from the TM Landsat 5 and OLI Landsat 8 sensor systems, and tools from the MapBiomas project from the 5.0 collection. The MNDWI analysis identified the increase in the surface area of the reservoir over the years, noting that the years 2000 and 2013 showed greater water stress with a reduction in the index values. The NDWI, SAVI and IAF indexes indicated a sparse and dry vegetation cover with low humidity for the years 2000 and 2013, however, there was an increase in vegetative vigor and the presence of higher humidity for the year 2020. data obtained for land use and occupation by the MapBiomas project, indicating that there was an increase in areas for agriculture and pasture around the reservoir between 2000 and 2013, as well as an increase in its water surface.Keywords: biophysical indices; water resource; remote sensing.

2021 ◽  
Vol 1 (2) ◽  
Van Anh TRAN ◽  
Thi Le LE ◽  
Nhu Hung NGUYEN ◽  
Thanh Nghi LE ◽  
Hong Hanh TRAN

Vietnam is an Asian country with hot and humid tropical climate throughout the year. Forestsaccount for more than 40% of the total land area and have a very rich and diverse vegetation.Monitoring the changes in the vegetation cover is obviously important yet challenging, considering suchlarge varying areas and climatic conditions. A traditional remote sensing technique to monitor thevegetation cover involves the use of optical satellite images. However, in presence of the cloud cover,the analyses done using optical satellite image are not reliable. In such a scenario, radar images are auseful alternative due to the ability of radar pulses in penetrating through the clouds, regardless of day ornight. In this study, we have used multi temporal C band satellite images to monitor vegetation coverchanges for an area in Dau Tieng and Ben Cat districts of Binh Duong province, Mekong Delta,Vietnam. With a collection of 46 images between March 2015 and February 2017, the changes of fiveland cover types including vegetation loss and replanting in 2017 were analyzed by selecting two cases,using 9 images in the dry season of 3 years 2015, 2016 and 2017 and using all of 46 images to conductRandom Forest classifier with 100, 200, 300 and 500 trees respectively. The result in which the modelwith nine images and 300 trees gave the best accuracy with an overall accuracy of 98.4% and a Kappaof 0.97. The results demonstrated that using VH polarization, Sentinel-1 gives quite a good accuracy forvegetation cover change. Therefore, Sentinel-1 can also be used to generate reliable land cover mapssuitable for different applications.

2021 ◽  
Benjamin Schumacher ◽  
Marwan Katurji ◽  
Jiawei Zhang ◽  
Peyman Zawar-Reza ◽  
Benjamin Adams ◽  

Abstract. Thermal Image Velocimetry (TIV) is a near-target remote sensing technique for estimating two- dimensional near-surface wind velocity based on spatiotemporal displacement of fluctuations in surface brightness temperature captured by an infrared camera. The addition of an automated parameterization and the combination of ensemble TIV results into one output made the method more suitable to changing meteorological conditions and less sensitive to noise stemming from the airborne sensor platform. Three field campaigns were carried out to evaluate the algorithm over turf, dry grass and wheat stubble. The derived velocities were validated with independently acquired observations from fine wire thermocouples and sonic anemometers. It was found that the TIV technique correctly derives atmospheric flow patterns close to the ground. Moreover, the modified method resolves wind speed statistics close to the surface at a higher resolution than the traditional measurement methods. Adaptive Thermal Image Velocimetry (A-TIV) is capable of providing contact-less spatial information about near-surface atmospheric motion and can help to be a useful tool in researching turbulent transport processes close to the ground.

2021 ◽  
Vol 936 (1) ◽  
pp. 012023
Bangun Muljo Sukojo ◽  
Noorlaila Hayati ◽  
Baisus Sa’adatul Usriyah

Abstract Data containing information on the terrain elevation model is necessary for several uses related to human activities, such as development planning, spatial planning, disaster modeling, disaster mitigation planning, land productivity estimation, etc. Information about the ground elevation can be presented in a 3-dimensional topographical model such as Digital Terrain Model (DTM). There are several technologies used to form DTM data, including by using LiDAR and radar satellites (Sentinel-1). The hydro enforcement method is used to process DTM with LiDAR data by modifying the elevation value of LiDAR data in water areas during data processing. The height of this feature is modified digitally to achieve hydrological connectivity. This method aims to produce a DTM according to the principles of hydro enforcement and hydro flatten. While for processing DTM radar data, the InSAR method is used. InSAR is a remote sensing technique to extract three-dimensional information from the earth’s surface with the phase of radar waves. Additional data of morphological information and break lines were added to provide more representative information on the actual situation. The result of this research is the value of vertical geometry accuracy (LE90) of DTM to RBI data with a scale of 1:25,000. In this research, 5 kinds of DTM have been successfully formed with LE90 vertical accuracy values are as follows: LiDAR DTM with LE90 of 4.614 m; InSAR DTM with LE90 of 9.583 m; InSAR breakline with LE90 of 9.433 m; InSAR RBI assimilation with LE90 of 2.532 m; and InSAR DTM-LiDAR assimilation with LE90 of 4.077 m. DTM with the highest accuracy based on Topographic Map (RBI) 1:25,000 is InSAR DTM RBI assimilation and the lowest accuracy is DTM InSAR without breakline and assimilation data.

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1599
Linshan Tan ◽  
Kaiyuan Zheng ◽  
Qiangqiang Zhao ◽  
Yanjuan Wu

Understanding the spatial and temporal variations of evapotranspiration (ET) is vital for water resources planning and management and drought monitoring. The development of a satellite remote sensing technique is described to provide insight into the estimation of ET at a regional scale. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to calculate the actual ET on a daily scale from Landsat-8 data and daily ground-based meteorological data in the upper reaches of Huaihe River on 20 November 2013, 16 April 2015 and 23 March 2018. In order to evaluate the performance of the SEBAL model, the daily SEBAL ET (ETSEBAL) was compared against the daily reference ET (ET0) from four theoretical methods: the Penman-Monteith (P-M), Irmak-Allen (I-A), the Turc, and Jensen-Haise (J-H) method, the ETMOD16 product from the MODerate Resolution Imaging Spectrometer (MOD16) and the ETVIC from Variable Infiltration Capacity Model (VIC). A linear regression equation and statistical indices were used to model performance evaluation. The results showed that the daily ETSEBAL correlated very well with the ET0, ETMOD16, and ETVIC, and bias between the ETSEBAL with them was less than 1.5%. In general, the SEBAL model could provide good estimations in daily ET over the study region. In addition, the spatial-temporal distribution of ETSEBAL was explored. The variation of ETSEBAL was significant in seasons with high values during the growth period of vegetation in March and April and low values in November. Spatially, the daily ETSEBAL values in the mountain area were much higher than those in the plain areas over the study region. The variability of ETSEBAL in this study area was positively correlated with elevation and negatively correlated with surface reflectance, which implies that elevation and surface reflectance are the important factors for predicting ET in this study area.

2021 ◽  
Kilian Vos ◽  
Wen Deng ◽  
Mitchell D. Harley ◽  
Ian L. Turner ◽  
Kristen D. Splinter

Abstract. Sandy beaches are unique environments composed of unconsolidated sediments that are constantly reshaped by the action of waves, tides, currents, and winds. The most seaward region of the dry beach, referred to as the beach face, is the primary interface between land and ocean and is of fundamental importance to coastal processes, including the dissipation and reflection of wave energy at the coast, and the exchange of sediment between the land and sea. The slope of the beach-face is a critical parameter in coastal geomorphology and coastal engineering, necessary to calculate the total elevation and excursion of wave run-up at the shoreline. However, datasets of the beach-face slope remain unavailable along most of the world’s coastlines. This study presents a new dataset of beach-face slopes for the Australian coastline derived from a novel remote sensing technique. The dataset covers 13,200 km of sandy coast and provides an estimate of the beach-face slope at every 100 m alongshore, accompanied by an easy to apply measure of the confidence of each slope estimate. The dataset offers a unique view of large-scale spatial variability in beach-face slope and addresses the growing need for this information to predict coastal hazards around Australia. The beach-face slope dataset and relevant metadata are available at (Vos et al., 2021)

2021 ◽  
Vol 11 (22) ◽  
pp. 10701
Rhushalshafira Rosle ◽  
Nik Norasma Che’Ya ◽  
Yuhao Ang ◽  
Fariq Rahmat ◽  
Aimrun Wayayok ◽  

This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages; weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further.

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